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
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.
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.
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	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 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	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	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	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotics self-play curriculum, not deployment safety 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	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	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Deals with autonomous driving policies, not manufacturing or industrial vision.
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.
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.
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	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	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical communication capacity analysis, not deployment safety.
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	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Examines scaling behavior, not model compression or efficiency.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Domain-specific acoustic corpus, not general LLM evaluation.
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	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	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	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	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	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	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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a decoding framework, not evaluation methodology.
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.
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.
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	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Evaluates RL toy tasks, not industrial visual inspection.
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	AI agents via arxiv · 15 Oct, 15:06	Not relevant Investigates RL policies in gridworlds, not LLM agents.
Exact Cut Complexity of Equal-Length Proportional Cake Cutting This paper establishes the exact cut complexity required for equal-length proportional cake cutting among multiple agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Purely theoretical mathematics, not AI deployment safety.
Exact Cut Complexity of Equal-Length Proportional Cake Cutting This paper establishes the exact cut complexity required for equal-length proportional cake cutting among multiple agents. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Mathematical algorithmic game theory, not LLM-based autonomous agents.
Exact Cut Complexity of Equal-Length Proportional Cake Cutting This paper establishes the exact cut complexity required for equal-length proportional cake cutting among multiple agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical cut complexity analysis, not an LLM benchmark.
ProtModernBERT: Compute-Efficient Protein Language Modeling This paper introduces an efficient encoder-only protein language model incorporating modern architectural updates to lower training costs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on biological sequence modeling, not deployment safety.
ProtModernBERT: Compute-Efficient Protein Language Modeling This paper introduces an efficient encoder-only protein language model incorporating modern architectural updates to lower training costs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates a specific model, does not propose new benchmarks.
ProtModernBERT: Compute-Efficient Protein Language Modeling This paper introduces an efficient encoder-only protein language model incorporating modern architectural updates to lower training costs. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on protein language modeling, not tool-using agents.
Does the Linguistic Register of Thinking Matter? Wenyan as a Compact Reasoning Register for Chinese LLMs This paper investigates using Classical Chinese as a compact intermediate reasoning register to reduce token length in Chinese language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on token efficiency, not safety or guardrails.
Does the Linguistic Register of Thinking Matter? Wenyan as a Compact Reasoning Register for Chinese LLMs This paper investigates using Classical Chinese as a compact intermediate reasoning register to reduce token length in Chinese language models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Exclusively text-based language model reasoning.
Does the Linguistic Register of Thinking Matter? Wenyan as a Compact Reasoning Register for Chinese LLMs This paper investigates using Classical Chinese as a compact intermediate reasoning register to reduce token length in Chinese language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Uses existing benchmarks, does not propose evaluation methods.
Does the Linguistic Register of Thinking Matter? Wenyan as a Compact Reasoning Register for Chinese LLMs This paper investigates using Classical Chinese as a compact intermediate reasoning register to reduce token length in Chinese language models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on natural language reasoning, not code generation.
Does the Linguistic Register of Thinking Matter? Wenyan as a Compact Reasoning Register for Chinese LLMs This paper investigates using Classical Chinese as a compact intermediate reasoning register to reduce token length in Chinese language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve external retrieval or document grounding.
Does the Linguistic Register of Thinking Matter? Wenyan as a Compact Reasoning Register for Chinese LLMs This paper investigates using Classical Chinese as a compact intermediate reasoning register to reduce token length in Chinese language models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Investigates linguistic registers for reasoning, not tool-using agents.
PRISMATIC : Prescription Risk Inspection System for Multi-Agent Treatment Interaction in Clinical Decision-Making This paper proposes a multi-agent system designed to reduce clinical medication errors through dynamic collaborative coordination. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a multi-agent framework, not general LLM benchmarking.
PRISMATIC : Prescription Risk Inspection System for Multi-Agent Treatment Interaction in Clinical Decision-Making This paper proposes a multi-agent system designed to reduce clinical medication errors through dynamic collaborative coordination. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Applies to medical prescription workflows, not software code generation.
PRISMATIC : Prescription Risk Inspection System for Multi-Agent Treatment Interaction in Clinical Decision-Making This paper proposes a multi-agent system designed to reduce clinical medication errors through dynamic collaborative coordination. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Processes clinical text records, not multimodal data.
PRISMATIC : Prescription Risk Inspection System for Multi-Agent Treatment Interaction in Clinical Decision-Making This paper proposes a multi-agent system designed to reduce clinical medication errors through dynamic collaborative coordination. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not focus on information retrieval or RAG architectures.
PRISMATIC : Prescription Risk Inspection System for Multi-Agent Treatment Interaction in Clinical Decision-Making This paper proposes a multi-agent system designed to reduce clinical medication errors through dynamic collaborative coordination. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Not about model pruning, distillation, or serving efficiency.
LabOPBench: A Multimodal Benchmark for Evidence-Grounded Laboratory Decision Making This paper introduces a multimodal benchmark evaluating AI models on evidence-grounded decision making in chemistry and materials laboratories. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Introduces an evaluation benchmark, not an autonomous agent system.

4,201–4,260 of 13,754← Previous Next →