The article presents a tutorial on constructing an OpenHarness-style agent runtime, detailing the implementation of core components such as tool use, typed tool schemas, permissions, lifecycle hooks, memory management, skills, context compaction, retry logic, cost tracking, and multi-agent coordination. It emphasizes an open control flow, allowing practitioners to experiment with the system without reliance on external APIs or infrastructure. This approach provides valuable insights for AI engineers looking to build flexible and scalable agent systems.
MarkTechPost77 d agofound 57 d ago#agent_runtime#tools#multi-agent
The Nex-N2-Mini-Ultra-Uncensored-Heretic model has been released, featuring 35 billion parameters and achieving a refusal rate of 5/100 with a Kullback-Leibler Divergence (KLD) of 0.0020. It is available in both Safetensors and GGUF formats, and utilizes Heretic version 1.2.0, which has shown better performance in terms of KLD compared to the newer version. This model's reduced censorship and enhanced performance metrics make it significant for practitioners looking to implement more flexible and capable LLMs in their applications.
Reddit r/LocalLLaMA77 d agofound 57 d ago#nex-n2#agent#model
The Qwen-AgentWorld-35B-A3B model has been benchmarked, achieving an overall score of 56.39 and notable performance in specific categories such as Search (36.69) and SWE (65.63). This model, part of the Qwen series, provides important insights for practitioners focused on coding tasks, as it demonstrates competitive capabilities in software engineering contexts compared to other models like Qwen3.5-397B-A17B. Understanding these benchmarks can guide developers in selecting appropriate models for their AI applications.
Reddit r/LocalLLaMA77 d agofound 57 d ago#qwen#coding#model
Anthropic has released Claude Tag, an integration that allows teams to utilize its AI within Slack by tagging @Claude for task assignments. This tool reportedly generates 65% of the internal code for Anthropic's product team, highlighting its potential to enhance productivity and streamline coding workflows in collaborative environments. For practitioners, this integration demonstrates the increasing utility of AI in real-time coding assistance and team collaboration.
The Decoder78 d agofound 57 d ago#claude#slack#internal_code
Nous Research has introduced the /learn command to the Hermes Agent Skills System, enabling the automatic generation of a standards-compliant SKILL.md from various sources such as local directories, document URLs, and past conversations. This enhancement allows the live agent to autonomously source content and create skills without manual input or a separate ingestion engine, streamlining the workflow for practitioners developing with LLMs by simplifying the skill creation process and reducing potential errors in documentation.
MarkTechPost78 d agofound 57 d ago#hermes#skills_system#workflows
Qwen has released the Qwen-AgentWorld-35B-A3B, a 35 billion parameter mixture of experts (MoE) model that activates approximately 3 billion parameters per token. This model is designed to simulate various environments, including MCP, terminal, software engineering, Android, web, and OS interactions, by predicting the next state based on an agent's actions. It is particularly relevant for practitioners focused on agent training, offline evaluation, and the development of synthetic environments for tool-use workflows.
Reddit r/LocalLLaMA78 d agofound 57 d ago#qwen#model#agent
The paper presents a reinforcement learning framework for Computer-Use Agents (CUAs) that utilizes autonomous vision-language evaluation as a scalable supervision signal, addressing the challenge of sparse reward signals in open-ended desktop environments. By modeling the imperfect feedback from a Vision-Language Model as a noisy binary reward channel, the authors implement a noise-corrected reward estimator for Proximal Policy Optimization, resulting in an average improvement of 12.6 percentage points in success rates over zero-shot performance. This approach highlights the potential of autonomous evaluation as a viable reward mechanism for training RL agents in graphical user interfaces, particularly when noise is accounted for in the reward estimation process.
arXiv cs.AI78 d agofound 57 d ago#reinforcement-learning#gui-agents#evaluation
The paper introduces a framework for governed shared memory in multi-agent LLM systems, addressing key issues such as unauthorized leakage and stale data propagation through defined primitives like scoped retrieval and provenance tracking. Implemented in MemClaw and evaluated with ArgusFleet, the system achieved 100% accuracy in provenance reconstruction and optimized write-to-visible latency to a single search round-trip, while revealing architectural challenges like asymmetric scope enforcement and pipeline ordering conflicts. This work underscores the necessity of explicit systems-level abstractions for effective multi-agent memory management in production environments, highlighting the importance of real-world evaluations to identify potential failures.
arXiv cs.AI78 d agofound 57 d ago#multi-agent-systems#memory-management
Metis is a self-evolving agent system that utilizes a hierarchical dual-representation memory to bridge text and code memory, allowing for improved experience reuse. A controlled study reveals that text and code representations offer complementary benefits, leading to Metis's architecture that organizes experiences into execution plans and callable tools. Evaluated on the AppWorld benchmark, Metis demonstrates up to 20.6% improvement in task accuracy and 22.8% reduction in execution costs compared to the ReAct system, highlighting its efficiency and effectiveness for practitioners developing interactive agents.
arXiv cs.AI78 d agofound 55 d ago#self-evolving agents#memory#text and code
The article presents an offline reinforcement learning framework aimed at optimizing SLAM (Scan/Label/Apply/Manifest) throughput control in warehouse environments. Key technical details include the use of a history-informed state representation, action space abstraction for delayed-impact control, and a reward function that incorporates both upstream and downstream metrics. The framework integrates multiple offline RL algorithms, with empirical results showing that the CQL policy improves system health by 22.97% and reduces average throttling duration by 3.18%, highlighting the effectiveness of offline RL in enhancing operational efficiency in warehouse settings.
arXiv cs.AI78 d agofound 55 d ago#reinforcement_learning#warehouse#slam
The workshop report from the "Engineering Reliable Autonomous Systems" (ERAS) held in June 2024 outlines key challenges and solutions in the field of autonomous systems engineering. It identifies critical areas such as verification and validation techniques, real-world engineering practices, and safe software architectures, culminating in a catalogue of challenges and proposed pathways for addressing them. This roadmap is significant for practitioners as it bridges the gap between academic techniques and practical implementation, fostering collaboration and advancing research in reliable autonomous systems.
arXiv cs.AI78 d agofound 55 d ago#autonomous_systems#engineering#reliability
A new approach to orchestration in coding agents using Bayesian control has been proposed, where a Bayesian controller dynamically manages tool-use decisions based on a belief over candidate correctness. This method was evaluated across six LLM generators and nine coding benchmarks, demonstrating superior performance in scenarios where verification is costly and critics provide informative yet imperfect feedback. The belief state produced by this controller offers an interpretable correctness score that surpasses traditional metrics like token probability and raw tool success, enhancing uncertainty quantification for practitioners in AI development.
arXiv cs.AI78 d agofound 55 d ago#coding#bayesian#agents
FlowR2A introduces a novel approach to multimodal driving planning by integrating scoring-based and anchor-based methods through a generative model that learns reward-conditioned action distributions. Utilizing a flow-matching decoder, it leverages dense trajectory-reward pairs to enhance the correlation between actions and their outcomes across multiple dimensions, including safety and compliance. This model achieves state-of-the-art performance on NAVSIM v1 and v2 benchmarks, offering high-quality proposals and improved sampling control, which is crucial for practitioners developing robust AI-driven driving systems.
arXiv cs.AI78 d agofound 57 d ago#driving planning#reward distribution#multimodal
A new benchmark study evaluates the performance of graphical user interface (GUI) agents versus command-line interface (CLI) agents in executing software tasks across 440 desktop tasks. The strongest GUI agent achieved a 59.1% full pass rate, while the best original-skill CLI agent reached 48.2%, with skill augmentation improving CLI success to 69.3%. This research highlights that GUI agents struggle with long-horizon workflows due to grounded interaction limitations, whereas CLI agents face challenges related to skill coverage and scalability, providing insights for practitioners developing AI agents in diverse execution environments.
arXiv cs.AI78 d agofound 57 d ago#gui#cli#benchmark
PixJail is a newly proposed framework designed for reproducible evaluation of Text-to-Image (T2I) jailbreak techniques, addressing the challenges of pipeline-level testing across multiple stages such as prompt transformation and safety filtering. The framework constructs paper-specific attack modules and evaluation pipelines, achieving an average reproduction error of 2.1% across eleven T2I jailbreak methods. This tool is significant for AI practitioners as it streamlines the reproduction process and enhances the reliability of benchmark comparisons in the rapidly evolving field of T2I jailbreaks.
arXiv cs.AI78 d agofound 55 d ago#text-to-image#jailbreak#evaluation#agents
MuTRAP is introduced as the first multi-trigger Trojan attack targeting LLM-assisted robot task planning systems. It leverages a method that injects backdoors using a small set of task-specific parameters while optimizing multiple-trigger words for various robotic applications, demonstrating vulnerabilities in current LLM-based planners. This research highlights critical security implications for practitioners working with LLMs in robotics, emphasizing the need for enhanced security measures in AI-driven task planning.
arXiv cs.AI78 d agofound 55 d ago#robotics#task_planning#security
TACTFUL is a novel tactile exploration framework designed for multi-fingered robots, enabling vision-free object localization and identification in confined environments. It employs a single policy trained on real hardware, achieving a 77% success rate and a 0.015 m average reconstruction error through a dynamic reward schedule that balances global exploration and local refinement. This approach highlights the potential of tactile sensing as a primary modality for object-level reasoning, offering significant implications for practitioners developing autonomous robotic systems.
arXiv cs.AI78 d agofound 55 d ago#tactile#robotics#exploration
Themis is a newly announced explainable AI framework designed for Reinforcement Learning (RL) that integrates human feedback to enhance safety and transparency. It supports over 200 environments and allows for easy configuration of experiments, demonstrating the ability to train reward models that align closely with true reward signals based on human preferences. This framework is significant for practitioners as it provides a scalable, user-friendly platform for conducting RL experiments with large participant groups while ensuring robust alignment and explainability, addressing critical challenges in RL safety.
arXiv cs.AI78 d agofound 55 d ago#reinforcement learning#human feedback#explainable ai
BioMedArena is an open-source toolkit designed to enhance the reproducibility of deep research agents in biomedical applications by standardizing evaluation processes across 166 benchmarks and 75 tools. It decouples six layers of evaluation, allowing for easy integration of new models with minimal engineering effort, and includes six agent harnesses and context-management strategies that improve performance across 12 backbones, achieving an average improvement of 15.01 percentage points over prior state-of-the-art results on eight benchmarks. This toolkit is significant for practitioners as it streamlines the evaluation and comparison of biomedical AI models, facilitating more consistent and reliable research outcomes.
arXiv cs.AI78 d agofound 55 d ago#open_source#agents#biomedical
The paper introduces ASALT (Adaptive State Alignment for Lateral Transfer), a novel method in multi-agent reinforcement learning (MARL) that addresses the challenge of transferring knowledge between source and target domains with mismatched state-space dimensionalities. ASALT utilizes observation-level and state-level adapters to map observations and states into a shared embedding space, enhancing sample efficiency and global returns in cooperative environments while reducing negative transfer. This advancement is significant for practitioners as it facilitates more effective policy transfer across heterogeneous domains, potentially improving the performance of MARL systems in diverse applications.
arXiv cs.AI78 d agofound 55 d ago#reinforcement#transfer#multi-agent
SP-Mind is introduced as the first autonomous AI agent specifically designed for spatial proteomics analysis, streamlining the process from raw multiplexed tissue imaging to phenotype discovery without requiring task-specific fine-tuning. It utilizes expert-curated biological analysis skills and specialized computational tools, and its performance is rigorously evaluated using SP-Bench, a benchmark consisting of 102 tasks across 18 categories, where SP-Mind demonstrates state-of-the-art results compared to existing biomedical agent baselines. This development is significant for practitioners as it enhances scalability and reproducibility in spatial proteomics research, facilitating more efficient analysis workflows in precision medicine.
arXiv cs.AI78 d agofound 57 d ago#proteomics#AI agent#workflow
The paper introduces the Goal-Oriented Dialogue Runtime (GODR), a conceptual framework designed to enhance conversational continuity in complex, multi-domain interactions involving interdependent objectives. GODR treats goals, task frames, and lifecycle states as first-class runtime objects, enabling better management of suspended, resumed, or invalidated goals, and is intended to work alongside existing orchestration frameworks rather than replace them. This framework is significant for practitioners as it addresses the challenges of maintaining conversational coherence in sophisticated dialogue systems, paving the way for more robust multi-agent interactions.
arXiv cs.AI78 d agofound 55 d ago#dialogue_systems#goal_oriented#llm
The article proposes a new model for e-commerce that leverages micro-transaction markets for verified product information, shifting the focus from product matching to acquiring trustworthy data. It outlines an architecture for this system where autonomous buyer agents can pay small amounts to access detailed product information, such as service histories and test reports, thus promoting genuine product quality and competitive pricing. The authors highlight key NLP challenges that arise from this model, including cost-optimal information acquisition and privacy-preserving persona modeling, suggesting these areas warrant further research and development in the field.
arXiv cs.AI78 d agofound 55 d ago#e-commerce#agents#micro-transactions
DeepBD is a novel workflow designed for the prioritization and diagnostic interpretation of genetic variants associated with birth defects. It integrates a pretrained evidence engine that evaluates patient-specific variant scores using structured rule evidence and phenotype-conditioned biological context, achieving Recall@1/3/5/10 scores of 0.658/0.882/0.912/0.929 on a benchmark of 18,622 cases, outperforming existing tools like Exomiser and DeepRare. This approach is significant for practitioners as it enhances the accuracy of variant prioritization by combining various evidence sources and LLM-assisted review, thereby improving diagnostic outcomes in clinical genetics.
arXiv cs.AI78 d agofound 55 d ago#llm#genetic#agents#workflow
The article introduces CAREB-MAS, a multi-agent framework designed to explore long-term social structures in agent societies using principles from Affect Control Theory and Social Identity Theory. The framework enables agents to develop egocentric identities and interact based on minimal protocols, leading to the emergence of five key phenomena associated with Differential Order, including stable labor specialization and emergent relational authority. This research highlights the potential of LLM-based simulations to provide insights into social dynamics and structures, which is crucial for practitioners aiming to model complex social interactions in AI systems.
arXiv cs.AI78 d agofound 55 d ago#multi_agent_systems#social_dynamics#llm
ATRIA is a multi-agent ECG reporting system designed to enhance clinical ECG report generation by decoupling interpretation and reporting, allowing for iterative context integration and bidirectional editing. It binds report claims to supporting evidence, flags unsupported statements, and enables clinicians to verify and revise findings, thereby reducing error propagation. Its architecture leverages existing ECG analysis models and is available as a cloud-based web service, making it ready for immediate deployment in clinical settings.
arXiv cs.AI78 d agofound 57 d ago#ecg-reporting#multi-agent-systems
The article introduces the Programmatic Skill Network (PSN), a framework for continual skill acquisition in embodied environments that utilizes large language models to create executable symbolic programs. Key mechanisms include structured fault localization, maturity-aware optimization, and canonical structural refactoring, which enhance skill stability and adaptability. Experiments conducted in MineDojo and Crafter show that PSN achieves effective skill reuse and generalization, highlighting its potential for advancing AI agents in dynamic task environments.
arXiv cs.AI78 d agofound 55 d ago#skill-acquisition#agents
The paper introduces the Latent Bridge, a novel continuous communication channel that enhances the interaction between a slow reasoning VLM (Qwen3-VL-8B-Thinking) and a fast reactive VLM (MiniCPM-o 4.5) by projecting the slow model's residuals into the fast model's input-embedding space, eliminating the need for text round-trips. Evaluated on 7 Atari games and a driving domain (MetaDrive), the Latent Bridge outperforms the traditional Text Bridge in several cases, notably improving performance in MsPacman by 57% and RoadRunner by 28%. This development is significant for practitioners as it offers a method to optimize real-time decision-making in AI agents, particularly in environments where latency and planning quality are critical.
arXiv cs.AI78 d agofound 57 d ago#game-agents#real-time#planning
The paper introduces AgenticInterpBench, a benchmark comprising 84 semi-synthetic transformer circuits with 163 component-level annotations, aimed at assessing language model (LM) agents' ability to explain identified circuits in mechanistic interpretability. It presents HyVE (Hypothesize, Validate, Explain), an agentic explainer that utilizes an iterative process to produce detailed explanations, demonstrating that while various LM backbones can generate useful insights, challenges in the validation phase hinder consistent performance. This work is significant for practitioners as it highlights the potential of LMs in circuit explanation while emphasizing the need for robust validation mechanisms to enhance interpretability in AI systems.
arXiv cs.AI78 d agofound 57 d ago#mechanistic interpretability#language model#agents#explanation
OmniPath is a newly announced framework designed to enhance wheelchair accessibility by integrating OpenStreetMap's network topology with high-density aerial LiDAR data to produce a detailed 3D model of pedestrian environments. The system analyzes surfaces in 0.5 meter increments to quantify physical friction points and assess compliance with ADA standards, categorizing hazards based on severity with an F1-score of 0.60 for severe and 0.58 for critical issues. This proactive auditing approach allows for the identification of accessibility challenges, transforming static maps into dynamic, actionable data for wheelchair users.
arXiv cs.AI78 d agofound 57 d ago#auditing#accessibility#environmental analysis
Maestro Order is introduced as a model-agnostic orchestration harness designed to enhance the reliability of language models by integrating four structural primitives: decompose, ensemble, verify, and recurse, alongside a budget-aware controller for compute allocation. The architecture operates by treating models as black-box solvers and employs a verifier ensemble to improve reliability, achieving significant improvements in reliability metrics (e.g., from 0.55 to 0.999) through strategic verification and voting mechanisms. This framework is crucial for practitioners as it provides a systematic approach to mitigate hallucinations in AI systems, optimizing resource usage while ensuring high reliability in problem-solving tasks.
arXiv cs.AI78 d agofound 55 d ago#orchestration#model_agnostic
This article presents a reinforcement learning approach for optimizing real-time event triggering at the Large Hadron Collider (LHC), addressing the limitations of static, hand-tuned trigger menus. The authors adapt Group-Filtered Policy Optimization (GFPO) for streaming control, achieving significant improvements in signal efficiency and in-tolerance rates for both total transverse energy and anomaly-detection triggers, with gains of up to 56% in real collision data without fine-tuning. This work is significant as it demonstrates the first application of RL for trigger control in real LHC data, potentially enhancing the efficiency of data collection in high-energy physics experiments.
arXiv cs.AI78 d agofound 55 d ago#reinforcement_learning#large_hadron_collider
The paper introduces Agentic long-term performance optimization (Agentic-LTPO), a bilevel optimization framework aimed at improving adaptive physical layer configurations in response to changing network policies and real-time constraints. It employs a multi-agent decision process for upper-level configuration generation and a closed-form beamformer for lower-level optimization, achieving a 57.2% improvement in long-term performance over traditional methods in a cell-free MIMO beamforming scenario. This approach is significant for practitioners as it enhances system adaptability and efficiency in dynamic network environments.
arXiv cs.AI78 d agofound 57 d ago#optimization#policy-driven#agentic-ai
LemonHarness is a newly announced integrated execution framework designed for long-horizon language model agents, establishing explicit execution boundaries to manage state changes during multi-step tasks. It constrains operations like file writes and artifact generation within a defined workspace, enhancing tracking and execution stability. Benchmark results show that LemonHarness_GPT-5.3-CodeX achieved 84.49% accuracy on Terminal-Bench 2.0, while the framework paired with the more powerful GPT-5.5 increased accuracy to 86.52%, highlighting its potential for improving the reliability of AI agents in complex workflows.
arXiv cs.AI78 d agofound 57 d ago#LLM#execution framework#workspace
The article presents a hierarchical multi-agent reinforcement learning (MARL) framework that integrates constraint manifold control to enforce hard safety constraints while enabling coordination among agents. This approach provides theoretical safety guarantees and achieves stationary learning dynamics, leading to stable and efficient training. Empirical results demonstrate competitive performance with nearly perfect safety rates, making it significant for practitioners focused on safety-critical applications in multi-agent systems.
arXiv cs.AI78 d agofound 57 d ago#multi-agent#reinforcement-learning#safety
The paper introduces Reward-Centered ReST-MCTS, a decision-making framework designed to enhance Monte Carlo tree search (MCTS) for robotic manipulation in uncertain environments. It decomposes feedback into multiple channels—rule, heuristic, neural, and value estimation—allowing for improved search bias and robustness against challenges such as sparse rewards and noisy transitions. This framework is significant for AI practitioners as it provides a structured approach to improving decision-making in high-uncertainty scenarios without necessitating a fully differentiable policy.
arXiv cs.AI78 d agofound 55 d ago#robotics#decision_making#MCTS
The article presents a framework for evolving expert "LLM + script" workflows into adaptable systems through a reversible migration path, termed the Strangler-Fig approach. This framework introduces a three-tier convertibility taxonomy (A/B/C) that assesses and routes legacy workflows into composable, typed, and auditable stages, addressing the need for dynamic adaptation based on feedback. This development is significant for practitioners as it provides a structured method to modernize existing workflows, enhancing their flexibility and responsiveness in AI applications.
arXiv cs.AI78 d agofound 55 d ago#workflow#llm#migration
The study evaluates the effectiveness of exact-match retrieval recall as a measure of policy utility in long-horizon tool-use agents, specifically using Qwen2.5-3B/7B classifiers within the tau-bench framework. It demonstrates that while a compact structured state improves macro-F1 scores by 0.13-0.17, the retrieval of policy clauses does not significantly differ from gold clauses in terms of classification performance, suggesting that reliance on exact-match recall may misrepresent the utility of retrieved policies. This finding emphasizes the need for practitioners to consider integrating retrieved policies in the classification loop rather than depending solely on recall metrics for evaluating retriever performance.
arXiv cs.AI78 d agofound 55 d ago#policy_signal#tool_use
The article introduces E-MRL (Evidence-driven Multimodal Reinforcement Learning), a novel framework designed to enhance 3D tumor analysis by addressing visual hallucinations in Vision-Language Models. E-MRL operates as a Markov Decision Process focusing on "diagnosis-localization-verification" and incorporates a cross-view consistency reward to ensure semantic alignment between diagnostic reports and visual evidence from 3D CT data. Experimental results on large-scale datasets show that E-MRL outperforms traditional Supervised Fine-Tuning and Reinforcement Learning approaches, improving diagnostic accuracy and reliability for practitioners in medical imaging and AI-driven diagnostics.
arXiv cs.AI78 d agofound 55 d ago#reinforcement_learning#multimodal#medical
The article introduces StockAgent, a multi-agent AI system utilizing large language models (LLMs) to simulate stock trading behaviors in response to external factors such as macroeconomic conditions and policy changes. StockAgent addresses the issue of test set leakage common in previous AI trading simulations, allowing for a more accurate analysis of trading behaviors and profitability under realistic market conditions. This framework provides insights that can enhance LLM-based investment strategies and stock recommendations, making it significant for practitioners in finance and AI.
arXiv cs.AI78 d agofound 55 d ago#llm#stock_trading#multi-agent
The article presents "NoContactNoWorries," a transformer-based multimodal framework designed to estimate binary contact states in robotic manipulation by integrating RGB-D vision with proprioceptive data. This approach addresses the limitations of traditional tactile sensors by enabling robots to infer contact through visual cues, thereby supporting downstream tasks such as in-hand object reorientation. Experimental validation in both simulation and real-world scenarios demonstrates the model's effectiveness and potential for enhancing dexterous manipulation capabilities in robotics.
arXiv cs.AI78 d agofound 55 d ago#robotics#manipulation#contact estimation
The article introduces a unified framework that integrates runtime verification and model-based diagnosis using the stream specification language LOLA. This framework allows for continuous online fault localization and detection by encoding system descriptions, health states, and observations within a single formalism, effectively handling both time-invariant and transient faults alongside nondeterministic observations. This development is significant for practitioners as it streamlines the fault management process in systems, reducing the need for separate toolchains and enhancing real-time diagnostics in AI applications.
arXiv cs.AI78 d agofound 55 d ago#runtime verification#diagnosis#lola
LLM-MINE is a proposed framework leveraging Large Language Models for the automatic extraction of Alzheimer's Disease and Related Dementias (ADRD) phenotypes from unstructured clinical notes. The framework demonstrated superior performance in phenotype clustering, achieving an Adjusted Rand Index (ARI) of 0.290 and Normalized Mutual Information (NMI) of 0.232, significantly surpassing traditional biomedical Named Entity Recognition (NER) and dictionary-based methods. This advancement is crucial for practitioners as it enhances the ability to mine clinically relevant signals from electronic health records, facilitating early detection and staging of ADRD.
arXiv cs.AI78 d agofound 55 d ago#llm#phenotype#extraction
SAFARI (Scaling long-horizon Agentic Fault AttRibution via active Investigation) is a new framework designed to enhance fault attribution in autonomous agents by utilizing a tool-augmented diagnostic loop, which allows for reading and searching trajectory segments alongside a persistent Short-Term Memory (STM). This approach decouples diagnostic accuracy from the limitations of LLM context windows, achieving a 20% improvement on the Who&When dataset and a 19% improvement on the TRAIL GAIA subset within specified token budgets. SAFARI maintains a precision of 0.58 even when diagnosing faults located 5x beyond the model's native context window, addressing a critical challenge in multi-step, multi-agent task execution.
arXiv cs.AI78 d agofound 55 d ago#multi-agent systems#fault attribution#diagnostics
The paper introduces a novel approach called structural certification for general agents, addressing the limitations of standard worst-case analysis in the big-world regime. By formalizing the concept that general agents cannot be universally capable, the authors present algorithms that use deep compositional goals to filter transitions, achieving an error bound of $\mathcal{O}(1/n) + \mathcal{O}(\delta)$ for goal-conditioned performance. This framework allows practitioners to certify the reliability of long-horizon planning in specific transitions, enhancing the deployment of general agents in complex environments.
arXiv cs.AI78 d agofound 55 d ago#agents#world models#certification
The article presents an evaluation of LAMBDA, a multi-agent data-analysis system, utilizing a three-layer human-AI grading cascade on 153 numerical QRData tasks from DSGym. The grading system achieved 100% precision with the strict grader and a 97% recall for the lenient grader against human labels, demonstrating effective strategies for distinguishing genuine outputs from grading artifacts. This work is significant for practitioners as it highlights the importance of hybrid grading approaches and the impact of iterative nudging on grading success, which can enhance the reliability of automated assessments in complex data analysis tasks.
arXiv cs.AI78 d agofound 55 d ago#data analysis#grading#multi-agent systems
The paper introduces a neuro-symbolic pipeline utilizing 2.5-D decomposition, which enables large language models (LLMs) to plan in a two-dimensional space while a deterministic executor handles vertical placements, significantly reducing systematic coordinate errors in spatial reasoning for autonomous construction. On the Build What I Mean benchmark, the GPT-4o-mini model integrated with this pipeline achieved a mean structural accuracy of 94.6%, outperforming GPT-4o and other competing systems, while demonstrating the ability to run on edge hardware like the Nemotron-3 120B with similar results. This approach is relevant for practitioners as it enhances LLM performance in tasks constrained by physical dimensions, potentially improving reliability in various autonomous construction applications.
arXiv cs.AI78 d agofound 55 d ago#llm#spatial_reasoning#2.5D
The article presents a framework for multi-agent LLMs utilizing subjective graphs to simulate uncertainty in social perception within educational settings. The agents employ individualized graphs to manage peer visibility and communication, updating Gaussian belief states through Bayesian fusion. Evaluated across 12 middle-school classrooms, the framework demonstrated a significant increase in collective ranking error, indicating persistent distortions in perceived academic standing, and outperformed traditional DeGroot configurations in maintaining opinion diversity, highlighting its relevance for practitioners interested in modeling social dynamics in AI systems.
arXiv cs.AI78 d agofound 55 d ago#social-perception#uncertainty
MoEngage has acquired technology that enables the deployment of individual AI agents for personalized customer interactions. This move signifies a shift towards leveraging AI for targeted marketing strategies, potentially enhancing customer engagement and retention through tailored experiences. Practitioners in AI and marketing will find implications for developing scalable, agent-based systems that can efficiently manage customer relationships.
TechCrunch AI78 d agofound 57 d ago#moengage#ai-agents#marketing
Ai2 has released the Tmax-27B, a terminal agent LLM built on Qwen3.6, utilizing DPPO for reinforcement learning, achieving approximately 43% on Terminal Bench 2.0 and 69% on TB Lite. The original model is 54 GB at FP16, but various quantized versions (ranging from 2-5 bits-per-weight) have been developed to fit consumer GPUs, with sizes from approximately 8.47 GB to 14.05 GB, making it more accessible for practitioners. This enables developers to leverage advanced terminal capabilities in AI applications without requiring high-end hardware.
Reddit r/LocalLLaMA78 d agofound 66 d ago#tmax-27b#terminal-agent#dppo
Anthropic has released Claude Tag, an AI assistant integrated into Slack, designed to continuously learn from organizational communications. This feature aims to enhance productivity by capturing contextual and institutional knowledge, which could streamline enterprise workflows. Its implementation may significantly impact how teams leverage AI to optimize collaboration and decision-making processes.
TechCrunch AI78 d agofound 66 d ago#anthropic#claude#slack
The article discusses the AI techniques used in the game Elden Ring, highlighting its reliance on low-tech methods rather than advanced machine learning models. It emphasizes the use of finite state machines and behavior trees for NPC decision-making, which allows for complex interactions without the computational overhead typically associated with modern AI approaches. This insight is valuable for practitioners interested in efficient game AI design that prioritizes performance and resource management over cutting-edge techniques.
Hacker News79 d agofound 57 d ago#ai#elden-ring
IRumAI is a novel reinforcement learning agent designed for Indian Rummy, utilizing Proximal Policy Optimization (PPO) and a dual-branch convolutional architecture. It achieves a 53.9% win rate against the strongest search-based opponent, demonstrating significant performance with a rapid inference time of 0.33 ms per action, which is over 7,000 times faster than existing heuristic methods. This advancement is crucial for practitioners as it offers a new approach to handling complex hidden-information games efficiently without relying on explicit search techniques.
arXiv cs.AI79 d agofound 65 d ago#reinforcement learning#rummy#PPO
PlanBench-XL is a newly introduced interactive benchmark designed to evaluate the long-horizon planning capabilities of LLM tool-use agents across 327 retail tasks utilizing 1,665 tools. It includes a mechanism to simulate real-world unpredictability through blocked or failing tool functions, revealing that even advanced models like GPT-5.4 struggle significantly under these conditions, achieving only 11.36% accuracy in severe blocking scenarios. This benchmark is crucial for practitioners as it highlights the challenges of adaptive planning in complex tool ecosystems, emphasizing the need for improved robustness in agent design.
arXiv cs.AI79 d agofound 65 d ago#planning#tool-use#LLM
The paper introduces AFTER, a benchmark comprising 382 enterprise tasks across six professional roles and 22 procedural skills, aimed at evaluating the transferability of skills in LLM agents. Results indicate that procedural memory enhances performance in industrial workflows, with a single refinement round yielding a 3.7-6.7 point improvement and achieving 73.1% cross-model test accuracy through multi-model execution traces. This research offers critical insights for practitioners on effectively implementing and assessing procedural memory systems in AI agent applications.
arXiv cs.AI79 d agofound 65 d ago#llm#memory#benchmark
The paper introduces SpecBench, a benchmark designed to assess an agent's capability to convert vague user intent into structured, executable specifications through interactive dialogue. It highlights the limitations of current agents in effectively collaborating with users and presents Buddy, a user-assistant agent that employs classical morphological analysis to systematically decompose user intent and facilitate decision-making. This approach underscores the importance of enhancing agent-user collaboration in the development of future AI systems, moving beyond mere code generation to a more nuanced understanding of user preferences.
arXiv cs.AI79 d agofound 65 d ago#user-assistant#intent#specifications
This paper introduces a diagnostic for identifying premature commitment in long-horizon LLM agents, where agents settle on a single interpretation of evidence too early in the reasoning process. The authors define representational commitment through cross-run hidden-state convergence and demonstrate its predictive power for behavioral consistency across models like Llama-3.1-70B, Qwen-2.5-72B, and Phi-3-14B, with high AUROC scores for detecting inconsistent trajectories. This research highlights a critical failure mode that impacts reasoning reliability, emphasizing the need for runtime monitoring and intervention strategies to mitigate variance without sacrificing accuracy.
arXiv cs.AI79 d agofound 65 d ago#premature commitment#llm#diagnosis
The paper introduces the concept of context drift in multi-agent LLM systems, identifying it as a key factor in hallucination occurrences due to discrepancies in agents' internal knowledge states. It presents the Context Divergence Score (CDS) for quantifying these discrepancies and proposes the Shared State Verification Protocol (SSVP) to facilitate periodic state exchanges, which significantly reduces hallucination rates by 30% while utilizing 58% fewer API calls compared to naive synchronization methods. This work reframes hallucination mitigation as a distributed systems challenge, emphasizing the importance of context synchronization in the design of multi-agent LLM architectures.
arXiv cs.AI79 d agofound 65 d ago#multi-agent#hallucination#context-drift
This study evaluates the effectiveness of hierarchical organization of action logs in Programming by Demonstration (PbD) for LLM agents. By grouping actions into labeled subgoals, the researchers found that this structure improved task completion rates in ambiguous natural-language tasks from 76.7% to 90.7%, while flat action logs showed minimal improvement. The findings suggest that for practitioners, implementing a hierarchical approach in PbD pipelines can significantly enhance the quality of procedural knowledge conveyed to LLMs, particularly in scenarios where task descriptions are vague.
arXiv cs.AI79 d agofound 65 d ago#programming by demonstration#llm#agents
The paper introduces a framework for understanding context management in long-horizon LLM agents, particularly focusing on how plans are managed and their impact on performance. It presents the concept of replay pairing to diagnose the decay of plan signals in the hidden state of Llama-3.1-70B, revealing that plans do not persist as state and are heavily dependent on remaining in context. The findings indicate that naive eviction of plans can significantly reduce task success rates, underscoring the importance of effective context management strategies for practitioners developing LLM applications.
arXiv cs.AI79 d agofound 65 d ago#context management#llm#plans
VADAOrchestra is a neurosymbolic framework designed to enhance decision-making workflows by integrating Large Language Models (LLMs) with logical reasoning. It utilizes a hybrid architecture where an LLM-based orchestrator incrementally adapts workflows based on user queries and data sources, encoding these workflows as Datalog+/- logic programs. This approach facilitates verifiable reasoning and scalability, allowing for complex reasoning over large datasets while maintaining auditability and reproducibility, as evidenced by evaluations on real-world financial use cases.
arXiv cs.AI79 d agofound 65 d ago#neurosymbolic#reasoning workflows#LLM
The article introduces Hypothesis-Driven Skill Optimization (HDSO), a framework designed to enhance action-oriented LLM agents without modifying their weights. HDSO operates with a frozen skill curator and executor, utilizing a structured process of hypothesis generation, validation, and skill approval that leads to performance improvements of +6.9 average success rate points for Qwen3-8B and +4.0 points for Qwen3.6-27B in the ALFWorld environment. This methodology emphasizes a controlled skill lifecycle, which is crucial for practitioners aiming to implement reliable and auditable skill integration in LLMs while mitigating the risks associated with noisy data.
arXiv cs.AI79 d agofound 65 d ago#skill optimization#LLM#hypothesis-driven
The article presents a design agent utilizing a Large Language Model (LLM) to orchestrate the design of a silicon-on-insulator (SOI) 2x2 directional coupler, where the LLM proposes gap values and assesses convergence while deterministic solvers handle the physics. The design process incorporates a frequency-domain eigenmode solver for coupling coefficient estimation and a Finite-Difference Time-Domain (FDTD) stage for validation, achieving a cross fraction of 0.498, closely aligning with the target of 0.500. This approach demonstrates the potential of LLMs in enhancing the design workflow of photonic devices by integrating numerical simulation techniques, which could streamline development for practitioners in the field.
arXiv cs.AI79 d agofound 60 d ago#llm#design#agent#simulation
The article presents NOEM$^{3}$A, a neuro-symbolic framework designed to enhance multi-intent understanding in mobile agents by integrating a lightweight intent ontology with compact language models like TinyLlama and Llama-3.2-3B. This approach employs a token-level decoding prior and Semantic Intent Similarity (SIS) to improve intent prediction accuracy while maintaining low latency and privacy. Experimental results on the MultiWOZ 2.3 dataset demonstrate significant improvements in performance metrics such as exact match and Slot-F1, indicating that symbolic alignment can effectively enhance on-device natural language understanding (NLU).
arXiv cs.AI79 d agofound 60 d ago#intent understanding#mobile agents#neuro-symbolic
PulseCX is a newly introduced framework designed to address the limitations of conversational AI agents in Customer Experience (CX) by breaking the Closed-World Assumption. It utilizes a Decay-Aware Temporal Knowledge Graph (DA-TKG) and an asynchronous agent architecture, achieving less than 10ms overhead for information retrieval while enhancing Intent Resolution Rate (IRR) and customer satisfaction (s-CSAT) in rapidly changing contexts. This approach is significant for practitioners as it allows for real-time adaptation to external trends, improving the responsiveness and effectiveness of AI-driven customer interactions.
arXiv cs.AI79 d agofound 65 d ago#conversational ai#customer experience#dynamic environments
The article introduces Latent Goal Prediction from Language (LAGO), a novel framework designed for model-based planning that predicts intermediate goal states from language instructions and action-conditioned rollouts within a shared latent space. LAGO addresses the limitations of traditional methods by dynamically decomposing instructions into tractable latent subgoals, allowing for coherent long-horizon planning without the degradation seen in prior approaches. This advancement is significant for practitioners as it combines the precision of visual goals with the flexibility of language, enhancing the effectiveness of planning in complex environments.
arXiv cs.AI79 d agofound 65 d ago#planning#language#goals
MetaPS is a new framework for adaptive programmatic strategy selection in financial markets, utilizing a library of executable strategies that respond to changing market conditions. It employs a simulation-guided approach to identify optimal strategy-state pairs, which are then used for supervised fine-tuning, enhancing performance across models ranging from 0.8B to 9B parameters. The framework shows significant improvements over fixed-strategy baselines and direct decision-making agents, indicating that market simulations can effectively provide targeted supervision for developing interpretable and adaptable trading strategies.
arXiv cs.AI79 d agofound 65 d ago#market agents#strategy selection#LLM
The article presents a decentralized traffic management approach for Advanced Air Mobility (AAM) corridors using multi-agent reinforcement learning (MARL). The developed policies effectively manage traffic in complex multi-corridor environments, demonstrating strong zero-shot transfer across varying traffic densities and network geometries without centralized coordination. This advancement is significant for practitioners as it offers a scalable solution to coordinate high-density autonomous aircraft operations, enhancing flexibility in trajectory planning while ensuring safety and efficiency.
arXiv cs.AI79 d agofound 60 d ago#traffic management#autonomous#multi-agent
The paper introduces JPPD, a Joint Prediction-Planning Diffusion framework designed for low-speed autonomous navigation in shared spaces, which integrates trajectory prediction and robot planning into a single conditional trajectory generation problem using a causal Transformer with cross-trajectory attention. Key innovations include differentiable safety potential guidance to replace traditional heuristic methods and conditional flow matching to enhance multimodal trajectory diversity while reducing inference steps. This approach has been validated through various simulations, demonstrating improvements in safety and runtime efficiency compared to conventional prediction-then-planning methods, making it significant for practitioners in intelligent transportation systems.
arXiv cs.AI79 d agofound 60 d ago#autonomous systems#trajectory planning#safety
PAPERCLAW is a multi-agent system designed to automate the research process from literature curation to paper writing. It operates through an iterative propose-test-reflect loop, utilizing a full-lifecycle memory to maintain context, allowing for pausing and resuming projects. The system integrates a human-in-the-loop mechanism for refinement and has been evaluated against an LLM judge, demonstrating its capability to produce high-quality research papers autonomously and with human input, which is significant for practitioners aiming to enhance research efficiency and output quality in AI.
arXiv cs.AI79 d agofound 65 d ago#multi_agent_system#autonomous_research#paper_generation
The paper presents a methodology for autonomous agents to develop a linguistic convention for referring to entities in their environment through decentralized communication. It demonstrates the scalability and robustness of this approach across 37 diverse datasets, showing that the emergent vocabulary can adapt to perceptual variations and environmental changes. This work is significant for AI practitioners as it provides insights into self-organizing systems that can enhance communication strategies in multi-agent environments.
arXiv cs.AI79 d agofound 60 d ago#emergent communication#autonomous agents#linguistic convention
Libretto is a newly introduced framework designed for symbolic music generation and revision, utilizing an LLM-native grammar that incorporates explicit structural elements such as onset slots and bar-level organization. It evaluates generated music within a calibrated statistical space based on various musical dimensions, including rhythm, harmony, and texture, facilitating tasks like gap filling and reference-guided generation. This framework enhances the ability of LLM agents to produce and manipulate music in a structured, measurable manner, making it a valuable tool for practitioners focused on integrating AI with music composition and analysis.
arXiv cs.AI79 d agofound 60 d ago#llm#music generation#agents
The paper presents a multi-agent deep reinforcement learning framework designed to optimize pricing and incentivization strategies in multimodal transportation networks. It employs two RL agents: one representing a public authority focused on enhancing equity and efficiency, and another representing a shared mobility service provider aiming to maximize revenue. Numerical experiments indicate that the proposed approach can reduce congestion peaks, decrease commuter costs by approximately 20%, lower emissions by about 10%, and nearly double public transport profits, thereby facilitating a more equitable distribution of transportation benefits. This framework offers a valuable decision-support tool for practitioners engaged in sustainable mobility planning.
arXiv cs.AI79 d agofound 60 d ago#reinforcement learning#transportation
SPARC is a newly introduced multi-agent system designed for electrical circuit question answering, leveraging executable physics-based simulations for enhanced reasoning. It employs LLM agents to create, execute, and analyze simulation programs, achieving an accuracy of 83%, which represents up to a 58% absolute improvement over existing baselines. This advancement is significant for practitioners as it facilitates more reliable and accurate responses to complex circuit-related queries, while also enabling systematic error diagnosis.
arXiv cs.AI79 d agofound 65 d ago#qa#circuit diagrams#llm
The paper presents a quantum-assisted distributed artificial intelligence framework for the real-time orchestration of hybrid renewable microgrids, addressing combinatorial dispatch and coalition formation under strict deadlines. Utilizing Belief-Desire-Intention extended agents, the dispatch problem is framed as a quadratic unconstrained binary optimization (QUBO) problem and solved using a combination of quantum, quantum-inspired, and classical solvers, with the Quantum Approximate Optimization Algorithm (QAOA) achieving optimal dispatch in a 24-hour simulation. The framework demonstrates zero missed deadlines, a daily operational cost of 146.24 EUR with 97.83% renewable utilization, and highlights the importance of a belief-shaped storage valuation mechanism in optimizing costs, which underscores the potential for integrating quantum computing in energy management systems for enhanced efficiency.
arXiv cs.AI79 d agofound 65 d ago#quantum#distributed ai#microgrids
The paper introduces Process-Reward Tactic Evolution, a training framework designed for LLM agents to effectively manage long-horizon bioinformatics workflows using Galaxy. This framework utilizes a curriculum-based approach to train agents on workflow execution, incorporating a tactic library derived from verified workflow rollouts, which enhances the agent's ability to construct workflows, monitor execution, and ensure biological correctness. The evaluation demonstrates that this process-supervised tactic accumulation significantly improves workflow completion rates and execution efficiency compared to traditional no-memory and reflection-style approaches, highlighting its relevance for practitioners developing complex bioinformatics applications.
arXiv cs.AI79 d agofound 65 d ago#bioinformatics#workflow#llm
This survey presents an analysis of LLM-based agents, emphasizing their evolution from passive question answering to active task completion through the integration of execution harnesses. It decomposes agent systems into six runtime responsibilities—observation, context, control, action, state, and verification—and explores how these interact with foundational models, highlighting the importance of harness configurations on task efficiency and reliability. The findings underscore the need for a holistic approach to agent design that considers model-harness co-evolution, open challenges in evaluation, and safety, which are critical for practitioners developing advanced AI systems.
arXiv cs.AI79 d agofound 65 d ago#llm#task completion#agent systems
The article introduces "Agent-as-a-Router," a novel framework for routing coding tasks to the most suitable Large Language Models (LLMs) by addressing the information deficit in existing static routers. The framework, instantiated as ACRouter, employs a C-A-F loop (Context->Action->Feedback->Context) and includes components like an Orchestrator and a Verifier, achieving a 15.3% performance improvement over heuristic routers. It is evaluated using CodeRouterBench, featuring approximately 10,000 task instances from eight leading LLMs, demonstrating superior performance in minimizing cumulative regret on both in-distribution and out-of-distribution tasks, which is critical for optimizing resource allocation in multi-LLM environments.
arXiv cs.AI79 d agofound 65 d ago#routing#llm#performance
The article presents a new approach to oversight for LLM agents, arguing that traditional scalar risk prediction methods are inadequate for effective intervention. It introduces the concept of intervention advantage and formalizes it through prefix branching, demonstrating that action-conditioned control can significantly reduce control regret, achieving a reduction from 0.506 to 0.110 on the ALFWorld benchmark. This shift towards action-conditioned value estimation emphasizes the need for more nuanced oversight mechanisms in LLM deployment, enhancing the reliability and effectiveness of interventions.
arXiv cs.AI79 d agofound 65 d ago#llm#agents#oversight
NeuPAN is a novel real-time robot motion planner that employs an end-to-end model-based learning approach, directly mapping raw point cloud data to a latent distance feature space for collision-free motion generation. Key innovations include a tightly coupled perception-to-control framework and a plug-and-play proximal alternating-minimization network that incorporates point-level constraints, allowing for interpretable and efficient motion generation. NeuPAN has been evaluated on various robotic platforms in simulated and real-world scenarios, demonstrating superior accuracy, efficiency, and robustness compared to existing methods, making it highly relevant for practitioners working on navigation in complex environments.
arXiv cs.AI79 d agofound 58 d ago#robotics#navigation#model-based
The article introduces OpenBioRQ, a novel benchmark comprising 12,553 unsolved biomedical research questions across 12 domains, designed to evaluate the performance of agentic models in a retrieval-grounded setting. This benchmark uniquely focuses on the faithfulness and abstention of models when addressing open questions without predefined answers, revealing that even advanced models like Gemini-3-Pro, Opus-4.7, and GPT-5.5 only solve 29-60% of the hardest questions. The findings highlight significant challenges in model reliability and tool utilization, underscoring the need for improved methodologies in addressing complex biomedical inquiries.
arXiv cs.CL79 d agofound 58 d ago#agents#biomedical#benchmark
The article introduces SAGE (Spherical Adaptive Gate for memory Evolution), a novelty detection mechanism for agentic LLMs that optimizes memory evolution by scoring candidate facts using a von Mises-Fisher density estimator. SAGE effectively categorizes facts as ADD, NOOP, or uncertain, significantly reducing write-time reasoning and achieving a 3.4× reduction in API costs and 2.5× lower latency during the add phase on GPT-4o-mini. This approach enhances memory quality and system efficiency across various models by decreasing LLM calls by approximately 16-18% with minimal impact on output quality, making it a valuable tool for practitioners focused on memory management in LLMs.
arXiv cs.AI79 d agofound 58 d ago#memory#llm#novelty-detection
The article introduces ORBIT (Orthogonal Rotation-Based Intervention Technique), a training-free method for multi-attribute behavioral steering in language models, which utilizes singular value decomposition to construct a joint subspace for norm-preserving rotations. It addresses the limitations of existing methods that struggle with simultaneous steering of multiple attributes by implementing adaptive per-token gating and an optional additive boost for weak attributes. Evaluated on the TraitFactory and ToneBank benchmarks with models including Llama-3.2-3B, Qwen-2.5-7B, and Llama-3.1-8B, ORBIT demonstrates improved performance in maintaining output coherence while steering multiple attributes effectively, making it a significant advancement for practitioners in AI model control.
arXiv cs.CL79 d agofound 58 d ago#llm#behavioral-steering#orthogonal
AgentCAT is a multi-agent simulation system utilizing Large Language Models to enhance Computerized Adaptive Testing (CAT) by dynamically assessing examinee proficiency. The framework includes three modules: an examinee agent leveraging memory retrieval and Chain-of-Thought reasoning, a selection agent employing coarse-to-fine bucketing and knowledge graph exploration, and a supervisor ensuring convergence through dual-auditing. Validation on real-world datasets demonstrates that AgentCAT effectively estimates abilities and balances difficulty adaptation, making it a significant advancement for practitioners aiming to implement adaptive learning technologies.
arXiv cs.AI79 d agofound 65 d ago#testing#education#multi-agent
The article introduces CURIOBOT, a framework leveraging Large Language Models (LLMs) to implement adaptive linguistic interventions based on Berlyne's collative variables to enhance exploratory learning behavior. Through 270 tutoring dialogues across various model families and domains, curiosity-oriented interventions resulted in up to 2.4 times more conversational turns, indicating a significant increase in learner engagement without altering tutor-side instructional quality. This research highlights the potential of LLM-mediated dialogue as a scalable tool for investigating the impact of language on exploratory cognition, which is crucial for developing more effective AI tutoring systems.
arXiv cs.CL79 d agofound 58 d ago#llm#tutoring#exploratory-learning
The paper introduces a novel test-time scaling law for physical AI agents based on active inference, allowing for effective reasoning and generalization in unforeseen scenarios. This law dynamically updates the agent's policy via a soft Bayesian inference process that minimizes prediction errors, enabling learning beyond the training distribution. Simulation results indicate that this approach significantly outperforms traditional methods like Q-learning and Bayesian reinforcement learning in autonomous driving tasks, enhancing inference efficiency by over 36%.
arXiv cs.AI79 d agofound 65 d ago#active inference#ai agents#scaling laws
Nous is a new predictive memory architecture that conceptualizes knowledge as prediction rather than storage, utilizing categorical probability distributions to maintain a world model. It updates beliefs using a Bayesian posterior based on information-theoretic surprise and records shifts in belief (delta) instead of storing facts directly. Evaluated against the LoCoMo benchmark, Nous demonstrates superior performance over A-MEM and BeliefMem in multiple memory tasks, achieving notable F1 scores with a GPT-4o-mini backbone, which is significant for practitioners seeking efficient long-term memory systems in AI agents without reliance on external databases.
arXiv cs.AI79 d agofound 65 d ago#memory#predictive model#Bayesian
The paper introduces Lexical Consensus, a framework for grounded word learning in AI agents, utilizing frozen DINOv2 visual embeddings and Carroll-style nonce words. Key findings reveal a robust perceptual-coherence gradient in lexical acquisition, where native categories are learned most easily, and perceptual distance is a significant predictor of acquisition accuracy, while semantic distance has negligible impact. This research highlights the importance of perceptual geometry in grounding lexical meanings, which is crucial for practitioners developing AI systems that require effective word learning and concept generalization.
arXiv cs.AI79 d agofound 60 d ago#lexical#word#learning
The article presents IRR-Drive, an adaptive multimodal reflection framework designed for autonomous driving, which integrates high-level reasoning with physical constraints to enhance trajectory planning. By generating a preliminary textual intention and predicting future semantic bird's-eye view (BEV) representations, IRR-Drive allows for rigorous self-correction and refinement of trajectories based on anticipated scene evolution. This approach achieves state-of-the-art results on the NAVSIM benchmark, highlighting its effectiveness in balancing planning performance with computational efficiency, particularly in complex environments.
arXiv cs.AI79 d agofound 60 d ago#autonomous-driving#multimodal#reflection
The paper introduces a measurement protocol for studying emergent consensus in LLM agent societies, focusing on a coupling gain (gamma) that quantifies per-agent opinion dynamics across five frontier models, revealing values between 0.15 and 0.43. It establishes that polarization in these systems is induced rather than spontaneous, and presents a validity diagnostic that differentiates genuine social dynamics from model artifacts, challenging previous findings in emergent consensus literature. This work provides critical insights for practitioners by offering tools to assess the reliability of consensus outcomes in LLM applications, emphasizing the importance of context-dependent coupling in understanding agent interactions.
arXiv cs.AI79 d agofound 60 d ago#llm#agent#consensus
BioInsight is a newly introduced multi-agent system designed for interactive biomedical knowledge discovery, moving beyond static report generation to create interactive evidence-centered interfaces. It organizes disease-specific evidence through various artifacts such as ranked pathways and citation-grounded reports, and evaluates its performance on standardized biomedical QA and protein-function reasoning tasks, achieving superior results. This development is significant for practitioners as it emphasizes the need for dynamic, interactive tools that enhance research decision-making by allowing users to explore and refine hypotheses based on comprehensive evidence.
arXiv cs.AI79 d agofound 65 d ago#multi-agent#biomedical#knowledge discovery
The article presents a design-time verification approach for agentic AI workflows, focusing on the orchestration of multiple LLM-based agents. It introduces a set of twelve structural rules to assess the compatibility of reusable building blocks within these workflows, implemented in a software prototype. Evaluation on 48 flawed workflows and 168 structurally altered variants demonstrated the verifier's effectiveness in detecting design violations, highlighting its potential for enhancing the safety and reliability of AI systems during the design phase.
arXiv cs.AI79 d agofound 65 d ago#llm#verification#workflows
The paper presents a reference architecture for skill-mediated LLM agents, detailing ten architectural patterns that facilitate the transition from static skill artefacts to dynamic skill-in-use. It introduces a framework comprising four responsibility layers: Supply Chain, Mediation, Execution Control, and Evidence & Feedback, evaluated through cross-instantiation across eight systems. This work is significant for practitioners as it offers a structured approach to integrating reusable agent skills, enhancing the robustness and adaptability of LLM applications.
arXiv cs.AI79 d agofound 65 d ago#agent skills#architecture#llm
AutoRAS is a newly proposed framework for the automated design of robust agentic systems, focusing on optimizing sequences of symbolic primitives that encode both structural connectivity and behavioral actions. The framework leverages execution-derived safety signals and flow-based objectives, demonstrating superior performance in both standard and adversarial settings with minimal degradation under attacks. This approach is significant for practitioners as it enhances the robustness of large language models in multi-agent environments, addressing vulnerabilities that can arise from external adversaries and internal failures.
arXiv cs.AI79 d agofound 65 d ago#llm#agents#robustness
StackPlanner is a hierarchical multi-agent system designed to enhance long-horizon collaboration in centralized architectures by incorporating explicit memory management. It features active task-level memory control and structured experience memory that allows agents to retrieve and utilize past coordination experiences, leveraging reinforcement learning for improved performance. This framework addresses issues of context bloat and error accumulation, making it significant for practitioners aiming to build more efficient and reliable multi-agent systems in complex tasks.
arXiv cs.AI79 d agofound 60 d ago#multi-agent systems#memory management#task experience
The article discusses the emergence of agentic AI systems, termed "AI scientists," which are capable of assisting in scientific discovery through tasks like literature synthesis, code generation, and data analysis. It emphasizes the need for redesigned scientific institutions to support the deployment of these multi-agent systems, illustrated by the prototype framework Denario, which enhances hypothesis generation and verification beyond human capabilities. This shift is significant for practitioners as it redefines the role of AI in research, necessitating new governance frameworks for accountability and interpretability in scientific processes.
arXiv cs.AI79 d agofound 65 d ago#ai scientists#multi-agent systems#discovery
The article presents PhoneBuddy, a novel training approach for developing open models capable of executing tasks on mobile phones. It integrates a real-app environment with a mock-app environment, PhoneWorld, to enhance training efficiency. Evaluation results indicate a significant improvement in task success rates, increasing from 36.67% to 45.33% on real phones and from 60.3% to 83.2% on the mock environment, highlighting the importance of combining real-app reinforcement learning with mock-app training to optimize agentic phone use.
arXiv cs.AI79 d agofound 60 d ago#agent#phone#training
The paper presents findings on the dynamics of visual shortcuts in large vision-language models (LVLMs) using reinforcement learning with verifiable rewards (RLVR). It introduces a grounding penalty parameter, lambda, which influences the formation and reversal of these shortcuts, revealing that shortcut reliance emerges abruptly and can be modulated by adjusting lambda. This research is significant for practitioners as it provides insights into the timing and strength of regularization strategies needed to maintain effective multimodal learning, thereby enhancing model robustness against perceptual biases.
arXiv cs.AI79 d agofound 65 d ago#reinforcement learning#visual shortcuts#multimodal
The article presents a proof-of-concept implementation of an infra-Bayesian reinforcement learning (RL) architecture that addresses the limitations of classical RL in environments with Knightian uncertainty. This new approach evaluates actions based on their worst-case outcomes rather than posterior expectations, leading to lower worst-case regret compared to traditional RL agents. This advancement is significant for practitioners as it enhances the robustness of RL agents in model misspecification scenarios, crucial for applications in AI safety and complex decision-making environments.
arXiv cs.AI79 d agofound 58 d ago#reinforcement-learning#robustness#agents
RetailBench is a newly introduced benchmark designed to evaluate the long-horizon decision-making capabilities of large language model (LLM) agents in realistic retail environments, specifically focusing on single-store supermarket operations over a thousand-day simulation. The benchmark assesses LLMs on various operational tasks, including pricing and inventory management, over a 180-day evaluation period, revealing that only a few models can sustain coherent decision-making, with all LLMs lagging behind an oracle policy in terms of net worth and sales outcomes. This benchmark is significant for practitioners as it highlights the challenges LLMs face in maintaining strategic stability in dynamic environments and provides a framework for developing more reliable autonomous decision-making systems.
arXiv cs.AI79 d agofound 58 d ago#llm#agents#retail#decision-making