Every paper checked — nothing hidden

All papers

Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,313 In brief24 More (on-topic)3,333 Not relevant13,754 Not assessed87 Older352 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
SolBehave: Evaluating the Behavioural Correctness of LLM-Generated Smart Contracts The paper introduces a benchmark using fuzzing and transaction replays to evaluate the behavioral correctness of generated smart contracts. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on smart contract correctness rather than autonomous planning agents.
PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards The paper introduces a debugging training algorithm that rewards localized semantic code edits alongside unit test execution. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents an RL training algorithm for code repair, not general agent frameworks.
PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards The paper introduces a debugging training algorithm that rewards localized semantic code edits alongside unit test execution. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a post-training technique for code models rather than an evaluation benchmark.
PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards The paper introduces a debugging training algorithm that rewards localized semantic code edits alongside unit test execution. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on code debugging via RL, not external document retrieval.
PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards The paper introduces a debugging training algorithm that rewards localized semantic code edits alongside unit test execution. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Optimizes bug-fix editing precision rather than safety guardrails or alignment.
PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards The paper introduces a debugging training algorithm that rewards localized semantic code edits alongside unit test execution. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL training objectives for code, not model compression or serving.
PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards The paper introduces a debugging training algorithm that rewards localized semantic code edits alongside unit test execution. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Studies text-based code generation and debugging without any multimodal aspects.
Beyond the Verdict: Agentic Code Judging with Verifiable Feedback The paper introduces a benchmark and model for evaluating program correctness and generating diagnostic counterexamples for competitive programming code. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Evaluates competitive programming code verifiers rather than safety risks or jailbreaks.
Beyond the Verdict: Agentic Code Judging with Verifiable Feedback The paper introduces a benchmark and model for evaluating program correctness and generating diagnostic counterexamples for competitive programming code. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on automated code verification rather than multi-step tool-using agents.
Jeva: System One Models from First Principles The paper proposes an alternative architecture for fast decision-making that handles missing choices, option permutation, and candidate scaling natively. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Architecture design for low-latency scoring, not safety guardrails or red-teaming.
Jeva: System One Models from First Principles The paper proposes an alternative architecture for fast decision-making that handles missing choices, option permutation, and candidate scaling natively. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a decision architecture rather than benchmarking or evaluating existing LLMs.
Jeva: System One Models from First Principles The paper proposes an alternative architecture for fast decision-making that handles missing choices, option permutation, and candidate scaling natively. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes a model architecture for fast classification decisions, not planning or tool-using agents.
BLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems The paper introduces an auditing framework that identifies failure steps in multi-agent systems by evaluating how information is utilized across handoffs. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-based multi-agent execution graphs, not multimodal models.
BLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems The paper introduces an auditing framework that identifies failure steps in multi-agent systems by evaluating how information is utilized across handoffs. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent execution handoffs, not document retrieval or knowledge grounding.
BLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems The paper introduces an auditing framework that identifies failure steps in multi-agent systems by evaluating how information is utilized across handoffs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a diagnostic attribution framework, not a general evaluation benchmark.
BLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems The paper introduces an auditing framework that identifies failure steps in multi-agent systems by evaluating how information is utilized across handoffs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies multi-agent fault attribution, not model compression or efficient inference.
BLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems The paper introduces an auditing framework that identifies failure steps in multi-agent systems by evaluating how information is utilized across handoffs. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses general multi-agent workflows rather than software engineering or coding assistants.
Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents The paper presents a runtime system that structures long-horizon agent memory around reusable progress objects rather than raw interaction history. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Evaluates web and OS agents rather than software development or coding.
Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents The paper presents a runtime system that structures long-horizon agent memory around reusable progress objects rather than raw interaction history. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Manages task state history rather than model distillation or weight quantization.
Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents The paper presents a runtime system that structures long-horizon agent memory around reusable progress objects rather than raw interaction history. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Optimizes agent context state rather than safety, guardrails, or alignment.
Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents The paper presents a runtime system that structures long-horizon agent memory around reusable progress objects rather than raw interaction history. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent history management, not document retrieval systems or external knowledge databases.
Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents The paper presents a runtime system that structures long-horizon agent memory around reusable progress objects rather than raw interaction history. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces an agent context runtime rather than an evaluation benchmark.
Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents The paper presents a runtime system that structures long-horizon agent memory around reusable progress objects rather than raw interaction history. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on runtime context architecture for agents rather than multimodal reasoning.
Where Do LLM Ensemble Gains Come From? Decomposing Candidate Value, Diversity, and Aggregation The paper analyzes how language model aggregation gains divide into candidate additions and context effects under different baseline comparison setups. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with text LLM aggregation without multimodal models or inputs.
Where Do LLM Ensemble Gains Come From? Decomposing Candidate Value, Diversity, and Aggregation The paper analyzes how language model aggregation gains divide into candidate additions and context effects under different baseline comparison setups. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on benchmark aggregation math rather than safety, red-teaming, or guardrails.
Where Do LLM Ensemble Gains Come From? Decomposing Candidate Value, Diversity, and Aggregation The paper analyzes how language model aggregation gains divide into candidate additions and context effects under different baseline comparison setups. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Analyzes ensemble aggregation metrics rather than retrieval systems, document ranking, or knowledge grounding mechanisms.
Where Do LLM Ensemble Gains Come From? Decomposing Candidate Value, Diversity, and Aggregation The paper analyzes how language model aggregation gains divide into candidate additions and context effects under different baseline comparison setups. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on static LLM ensemble output aggregation, not autonomous agent planning or multi-step execution.
Where Do LLM Ensemble Gains Come From? Decomposing Candidate Value, Diversity, and Aggregation The paper analyzes how language model aggregation gains divide into candidate additions and context effects under different baseline comparison setups. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies ensemble aggregation metrics, not model compression or efficient serving.
Where Do LLM Ensemble Gains Come From? Decomposing Candidate Value, Diversity, and Aggregation The paper analyzes how language model aggregation gains divide into candidate additions and context effects under different baseline comparison setups. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Paper examines general LLM ensemble outputs, not software engineering or code generation.
Bytes to Threats: Byte-Sequence Transformer For Malware Detection The paper presents a byte-level transformer model designed to detect malware directly from raw executable byte sequences. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a malware classifier rather than an LLM evaluation framework.
Bytes to Threats: Byte-Sequence Transformer For Malware Detection The paper presents a byte-level transformer model designed to detect malware directly from raw executable byte sequences. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on a malware classification model, not multi-step autonomous agents.
SEAR: Self-Evolving Agentic Robotics The paper presents a framework and benchmark to evaluate whether self-evolving robotic agents genuinely learn reusable skills across tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies agent learning and iteration curves, not small model compression.
SEAR: Self-Evolving Agentic Robotics The paper presents a framework and benchmark to evaluate whether self-evolving robotic agents genuinely learn reusable skills across tasks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotic agent evolution, not retrieval-augmented generation.
SEAR: Self-Evolving Agentic Robotics The paper presents a framework and benchmark to evaluate whether self-evolving robotic agents genuinely learn reusable skills across tasks. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotic agent learning systems, not multimodal model architectures.
SEAR: Self-Evolving Agentic Robotics The paper presents a framework and benchmark to evaluate whether self-evolving robotic agents genuinely learn reusable skills across tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Addresses general robotic manipulation, not industrial defect detection.
SEAR: Self-Evolving Agentic Robotics The paper presents a framework and benchmark to evaluate whether self-evolving robotic agents genuinely learn reusable skills across tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Evaluates learning efficiency in agents, not deployment safety guardrails.
Optimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning The paper introduces an adaptive diversity control framework that optimizes cooperative multi-agent reinforcement learning teams using episodic return feedback. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on traditional multi-agent reinforcement learning control, not language model agents.
Optimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning The paper introduces an adaptive diversity control framework that optimizes cooperative multi-agent reinforcement learning teams using episodic return feedback. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on MARL diversity tuning, not AI safety or deployment guardrails.
Optimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning The paper introduces an adaptive diversity control framework that optimizes cooperative multi-agent reinforcement learning teams using episodic return feedback. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Addresses multi-agent RL control, not industrial vision defect detection.
Optimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning The paper introduces an adaptive diversity control framework that optimizes cooperative multi-agent reinforcement learning teams using episodic return feedback. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a control framework for RL, not an LLM benchmark.
Self-Evolving Agent Harness The paper formalizes a framework and theoretical generalization bounds for evolving an agent's software harness without updating model weights. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent scaffolding updates, not model quantization or compact architectures.
Self-Evolving Agent Harness The paper formalizes a framework and theoretical generalization bounds for evolving an agent's software harness without updating model weights. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Addresses text-based software scaffolding, not vision or multimodal models.
Self-Evolving Agent Harness The paper formalizes a framework and theoretical generalization bounds for evolving an agent's software harness without updating model weights. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent harness evolution, not retrieval-augmented generation systems.
Self-Evolving Agent Harness The paper formalizes a framework and theoretical generalization bounds for evolving an agent's software harness without updating model weights. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes harness optimization theory rather than proposing an LLM evaluation benchmark.
Self-Evolving Agent Harness The paper formalizes a framework and theoretical generalization bounds for evolving an agent's software harness without updating model weights. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent software harness optimization, not software engineering models.
Back-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMs The paper examines back-translation using monolingual data as an alternative to continued pretraining for low-resource translation with language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies translation accuracy, not safety, hallucinations, or deployment guardrails.
Back-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMs The paper examines back-translation using monolingual data as an alternative to continued pretraining for low-resource translation with language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Investigates back-translation data, not retrieval-augmented generation systems.
Back-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMs The paper examines back-translation using monolingual data as an alternative to continued pretraining for low-resource translation with language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses natural language machine translation, not code generation or repair.
Back-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMs The paper examines back-translation using monolingual data as an alternative to continued pretraining for low-resource translation with language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies machine translation fine-tuning, not autonomous tool-use agents.
Back-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMs The paper examines back-translation using monolingual data as an alternative to continued pretraining for low-resource translation with language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on low-resource translation training, not benchmark design or LLM evaluation.
Back-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMs The paper examines back-translation using monolingual data as an alternative to continued pretraining for low-resource translation with language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals strictly with text translation, not multimodal models.
Back-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMs The paper examines back-translation using monolingual data as an alternative to continued pretraining for low-resource translation with language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Investigates training data for translation, not model pruning or distillation.
Read the Room Before Your Model Generates The paper introduces a decoding framework that dynamically selects sampling temperature based on geometric representations of conversational scenarios. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies decoding temperature adaptation, not software engineering or code models.
Read the Room Before Your Model Generates The paper introduces a decoding framework that dynamically selects sampling temperature based on geometric representations of conversational scenarios. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Studies decoding temperature calibration, not retrieval-augmented generation systems.
Read the Room Before Your Model Generates The paper introduces a decoding framework that dynamically selects sampling temperature based on geometric representations of conversational scenarios. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Proposes scenario-based temperature tuning, not model quantization or compact architectures.
Read the Room Before Your Model Generates The paper introduces a decoding framework that dynamically selects sampling temperature based on geometric representations of conversational scenarios. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Adapts generation temperature, not guardrails or jailbreak defenses.
Read the Room Before Your Model Generates The paper introduces a decoding framework that dynamically selects sampling temperature based on geometric representations of conversational scenarios. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on temperature sampling during text generation, not autonomous multi-step agents.
Read the Room Before Your Model Generates The paper introduces a decoding framework that dynamically selects sampling temperature based on geometric representations of conversational scenarios. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Addresses text generation decoding, not multimodal vision-language models.
Read the Room Before Your Model Generates The paper introduces a decoding framework that dynamically selects sampling temperature based on geometric representations of conversational scenarios. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on a decoding generation method rather than evaluation frameworks.
RoboSimStudio: An Interactive Toolkit for Rapid Real2Sim of Robot Manipulation Tasks The paper presents an interactive simulation toolkit that enables humans and code agents to iteratively calibrate robot manipulation physics. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on physics modeling, not small model distillation or inference efficiency.

14,281–14,340 of 17,313← Previous Next →