Every paper checked — nothing hidden

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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
MCAIR: Monitoring and Correcting Agent Unfaithfulness via Internal Representations The paper proposes a runtime monitoring method to detect and correct language model agents that deviate from intended procedures without reporting it. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on internal representation probing, not model compression or efficiency.
Interlock: From Local Correctness to Task Completion in Long-Horizon Agents The paper proposes a graph-based task synthesis framework to help long-horizon agents satisfy inter-dependent constraints and achieve complete task outcomes. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on task synthesis and evaluation, not efficient inference or pruning.
Interlock: From Local Correctness to Task Completion in Long-Horizon Agents The paper proposes a graph-based task synthesis framework to help long-horizon agents satisfy inter-dependent constraints and achieve complete task outcomes. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on general long-horizon agent execution, not code generation.
Interlock: From Local Correctness to Task Completion in Long-Horizon Agents The paper proposes a graph-based task synthesis framework to help long-horizon agents satisfy inter-dependent constraints and achieve complete task outcomes. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent task synthesis, not retrieval-augmented generation architectures.
Interlock: From Local Correctness to Task Completion in Long-Horizon Agents The paper proposes a graph-based task synthesis framework to help long-horizon agents satisfy inter-dependent constraints and achieve complete task outcomes. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Addresses long-horizon text agent tasks, not multimodal models.
Interlock: From Local Correctness to Task Completion in Long-Horizon Agents The paper proposes a graph-based task synthesis framework to help long-horizon agents satisfy inter-dependent constraints and achieve complete task outcomes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Evaluates long-horizon task synthesis, not deployment safety or guardrails.
Function-Space Temporal Self-Distillation for Policy Optimization The paper introduces a single-policy optimization framework combining privileged adaptation with stored historical next-token distributions for language model reasoning. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on policy optimization training algorithms rather than agent architecture.
Function-Space Temporal Self-Distillation for Policy Optimization The paper introduces a single-policy optimization framework combining privileged adaptation with stored historical next-token distributions for language model reasoning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a reinforcement learning algorithm rather than an evaluation benchmark.
Function-Space Temporal Self-Distillation for Policy Optimization The paper introduces a single-policy optimization framework combining privileged adaptation with stored historical next-token distributions for language model reasoning. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on language model reasoning optimization, not multimodal inputs.
Function-Space Temporal Self-Distillation for Policy Optimization The paper introduces a single-policy optimization framework combining privileged adaptation with stored historical next-token distributions for language model reasoning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Addresses RL training optimization, not retrieval-augmented generation systems.
Function-Space Temporal Self-Distillation for Policy Optimization The paper introduces a single-policy optimization framework combining privileged adaptation with stored historical next-token distributions for language model reasoning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Investigates policy distillation techniques, not safety, guardrails, or alignment risks.
Function-Space Temporal Self-Distillation for Policy Optimization The paper introduces a single-policy optimization framework combining privileged adaptation with stored historical next-token distributions for language model reasoning. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Deals with general policy optimization, not software engineering models.
PPAD-Completeness of Contiguous Envy-Free Cake Cutting with Additive Valuations The paper proves computational complexity results for contiguous envy-free cake-cutting and indivisible goods allocations under additive valuations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical computational complexity proof, not an LLM evaluation benchmark.
PPAD-Completeness of Contiguous Envy-Free Cake Cutting with Additive Valuations The paper proves computational complexity results for contiguous envy-free cake-cutting and indivisible goods allocations under additive valuations. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Theoretical algorithmic game theory paper on cake-cutting, not LLM agents.
PPAD-Completeness of Contiguous Envy-Free Cake Cutting with Additive Valuations The paper proves computational complexity results for contiguous envy-free cake-cutting and indivisible goods allocations under additive valuations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Proves PPAD-completeness in fair division, not AI safety deployment.
ReFormBench: Benchmarking the Stability of Response-Conditioned Format Correction in Large Language Models The paper presents a benchmark evaluating how reliably language models correct and maintain formatting requirements across changing instructions. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies response formatting constraints rather than tool-use planning agents.
C3: Rethinking Process Supervision via Confidence-Guided Credit Correction in Multi-Turn Reinforcement Learning The paper introduces a method that selectively adjusts positive credit for undesirable agent actions using the policy's confidence during multi-turn reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a training method, not a benchmark or evaluation framework.
C3: Rethinking Process Supervision via Confidence-Guided Credit Correction in Multi-Turn Reinforcement Learning The paper introduces a method that selectively adjusts positive credit for undesirable agent actions using the policy's confidence during multi-turn reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on reinforcement learning credit correction, not deployment safety or robustness.
RuleTrek: Benchmarking Agentic Rule Reasoning in Complex Scenarios The paper introduces a multi-domain benchmark to evaluate language model agents on identifying, understanding, and applying domain-specific rules across complex files. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Benchmarking rule deduction capabilities rather than safety, guardrails, or jailbreaks.
Runtime Monitoring of Supermartingale Certificates The paper proposes an algorithm using betting-based sequential tests to monitor supermartingale certificate violations during runtime execution of stochastic systems. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on dynamical system certificates, not LLM agents.
Runtime Monitoring of Supermartingale Certificates The paper proposes an algorithm using betting-based sequential tests to monitor supermartingale certificate violations during runtime execution of stochastic systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Deals with formal stochastic runtime monitors, not LLM benchmarks.
SceneAE: Scene Generation and Reconstruction in Queryable 3D-Anchored Latent Space The paper proposes a 3D-anchored latent space representation for simultaneous scene reconstruction and view generation. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on 3D computer vision rather than multimodal language reasoning.
SceneAE: Scene Generation and Reconstruction in Queryable 3D-Anchored Latent Space The paper proposes a 3D-anchored latent space representation for simultaneous scene reconstruction and view generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a generative latent architecture, not an evaluation benchmark.
SceneAE: Scene Generation and Reconstruction in Queryable 3D-Anchored Latent Space The paper proposes a 3D-anchored latent space representation for simultaneous scene reconstruction and view generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers 3D latent scene representation, not deployment safety.
SceneAE: Scene Generation and Reconstruction in Queryable 3D-Anchored Latent Space The paper proposes a 3D-anchored latent space representation for simultaneous scene reconstruction and view generation. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Presents 3D latent representation, not language model tool planning.
SceneAE: Scene Generation and Reconstruction in Queryable 3D-Anchored Latent Space The paper proposes a 3D-anchored latent space representation for simultaneous scene reconstruction and view generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on general 3D scene generation, not industrial inspection.
New Model, Old Habits: Behavior Transfer under Model Swapping in Multi-Session LLM Agents The paper analyzes model swapping in multi-session agents and proposes a history summarization method to preserve strong reasoning behaviors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Examines multi-session agent behavior transfer rather than presenting evaluation methodology.
New Model, Old Habits: Behavior Transfer under Model Swapping in Multi-Session LLM Agents The paper analyzes model swapping in multi-session agents and proposes a history summarization method to preserve strong reasoning behaviors. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Investigates behavioral history transfer rather than safety vulnerabilities or guardrails.
Uncertainty-Driven Self-Play for Robust Multi-Agent Driving Policies The paper introduces an uncertainty-driven self-play method where exploring agents target policy weaknesses to learn robust autonomous driving policies. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on autonomous driving motion planning, not LLM planning agents.
Uncertainty-Driven Self-Play for Robust Multi-Agent Driving Policies The paper introduces an uncertainty-driven self-play method where exploring agents target policy weaknesses to learn robust autonomous driving policies. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Deals with autonomous driving policies, not manufacturing or industrial vision.
Uncertainty-Driven Self-Play for Robust Multi-Agent Driving Policies The paper introduces an uncertainty-driven self-play method where exploring agents target policy weaknesses to learn robust autonomous driving policies. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates autonomous driving trajectories, not language model capabilities.
Uncertainty-Driven Self-Play for Robust Multi-Agent Driving Policies The paper introduces an uncertainty-driven self-play method where exploring agents target policy weaknesses to learn robust autonomous driving policies. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotics self-play curriculum, not deployment safety guardrails.
Learning Individual Scientific Taste with Privileged Hindsight Distillation The paper models individual scientific taste using criterion assessments and introduces privileged hindsight distillation to align proposals with researcher preferences. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on scientific proposal evaluation, not safety guardrails.
Learning Individual Scientific Taste with Privileged Hindsight Distillation The paper models individual scientific taste using criterion assessments and introduces privileged hindsight distillation to align proposals with researcher preferences. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a distillation method for research taste, not benchmarking.
Learning Individual Scientific Taste with Privileged Hindsight Distillation The paper models individual scientific taste using criterion assessments and introduces privileged hindsight distillation to align proposals with researcher preferences. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Models subjective preference distillation, not tool-using planning agents.
Learning Individual Scientific Taste with Privileged Hindsight Distillation The paper models individual scientific taste using criterion assessments and introduces privileged hindsight distillation to align proposals with researcher preferences. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on preference distillation, not small efficient models.
Articulation Upper Bounds for Language Models The paper introduces prompt optimization and recovery measurement tools to evaluate the limits of transmitting concepts between scaling language models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Purely focused on text concepts between language models.
Articulation Upper Bounds for Language Models The paper introduces prompt optimization and recovery measurement tools to evaluate the limits of transmitting concepts between scaling language models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Studies concept articulation limits, not code generation.
Articulation Upper Bounds for Language Models The paper introduces prompt optimization and recovery measurement tools to evaluate the limits of transmitting concepts between scaling language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address retrieval or external knowledge augmentation.
Articulation Upper Bounds for Language Models The paper introduces prompt optimization and recovery measurement tools to evaluate the limits of transmitting concepts between scaling language models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Examines scaling behavior, not model compression or efficiency.
Articulation Upper Bounds for Language Models The paper introduces prompt optimization and recovery measurement tools to evaluate the limits of transmitting concepts between scaling language models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies concept communication bounds, not autonomous planning agents.
Articulation Upper Bounds for Language Models The paper introduces prompt optimization and recovery measurement tools to evaluate the limits of transmitting concepts between scaling language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical communication capacity analysis, not deployment safety.
ONCship: A Large-Scale Underwater Acoustic Corpus for Ship Recognition This paper introduces a large-scale underwater acoustic recording dataset paired with vessel tracking data for ship recognition tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Domain-specific acoustic corpus, not general LLM evaluation.
ONCship: A Large-Scale Underwater Acoustic Corpus for Ship Recognition This paper introduces a large-scale underwater acoustic recording dataset paired with vessel tracking data for ship recognition tasks. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Underwater acoustic dataset for ships, not language model agents.
ONCship: A Large-Scale Underwater Acoustic Corpus for Ship Recognition This paper introduces a large-scale underwater acoustic recording dataset paired with vessel tracking data for ship recognition tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with ship acoustic classification, not AI deployment safety.
Recognizing Dialogue Ambiguity via Candidate Set Visual Grounding This paper introduces a task and benchmark for tracking candidate sets of referent objects across ambiguous interactive dialogue turns. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Studies multimodal dialogue, not code generation or software engineering.
Recognizing Dialogue Ambiguity via Candidate Set Visual Grounding This paper introduces a task and benchmark for tracking candidate sets of referent objects across ambiguous interactive dialogue turns. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Evaluates visual ambiguity tracking, not safety or guardrails.
Recognizing Dialogue Ambiguity via Candidate Set Visual Grounding This paper introduces a task and benchmark for tracking candidate sets of referent objects across ambiguous interactive dialogue turns. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on visual grounding tasks, not multi-step autonomous agents.
Recognizing Dialogue Ambiguity via Candidate Set Visual Grounding This paper introduces a task and benchmark for tracking candidate sets of referent objects across ambiguous interactive dialogue turns. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression or efficient serving.
Recognizing Dialogue Ambiguity via Candidate Set Visual Grounding This paper introduces a task and benchmark for tracking candidate sets of referent objects across ambiguous interactive dialogue turns. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Grounds text to image candidates, not external RAG.
Recognizing Dialogue Ambiguity via Candidate Set Visual Grounding This paper introduces a task and benchmark for tracking candidate sets of referent objects across ambiguous interactive dialogue turns. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant General dialogue visual grounding, not industrial visual inspection.
Mitigating Video Hallucinations via Attention-Guided Representation Perturbation This paper presents a training-free contrastive decoding framework using attention-guided perturbations to reduce hallucinations in video language models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on general Video LLM decoding, not industrial defect detection.
Mitigating Video Hallucinations via Attention-Guided Representation Perturbation This paper presents a training-free contrastive decoding framework using attention-guided perturbations to reduce hallucinations in video language models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on decoding strategies, not planning or tool-use agents.
Mitigating Video Hallucinations via Attention-Guided Representation Perturbation This paper presents a training-free contrastive decoding framework using attention-guided perturbations to reduce hallucinations in video language models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Addresses video language models, not software engineering.
Mitigating Video Hallucinations via Attention-Guided Representation Perturbation This paper presents a training-free contrastive decoding framework using attention-guided perturbations to reduce hallucinations in video language models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Training-free decoding technique, not model compression or small models.
Mitigating Video Hallucinations via Attention-Guided Representation Perturbation This paper presents a training-free contrastive decoding framework using attention-guided perturbations to reduce hallucinations in video language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not focus on external retrieval or RAG mechanisms.
Mitigating Video Hallucinations via Attention-Guided Representation Perturbation This paper presents a training-free contrastive decoding framework using attention-guided perturbations to reduce hallucinations in video language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a decoding framework, not evaluation methodology.
What Happens to the Goal? Distinct Failures of Policy Alignment in Goal Misgeneralization This paper analyzes three distinct failure modes where reinforcement learning agents misgeneralize goals despite competent behavior. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Investigates RL policies in gridworlds, not LLM agents.
What Happens to the Goal? Distinct Failures of Policy Alignment in Goal Misgeneralization This paper analyzes three distinct failure modes where reinforcement learning agents misgeneralize goals despite competent behavior. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression or inference efficiency.
What Happens to the Goal? Distinct Failures of Policy Alignment in Goal Misgeneralization This paper analyzes three distinct failure modes where reinforcement learning agents misgeneralize goals despite competent behavior. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Investigates policy alignment failure modes, not general LLM benchmarking.

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