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
Typed Decision Models on Tabular Data The paper compares typed probabilistic decision models with large language models on tabular data classification and format robustness. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Deals with structured tabular text data without multimodal visual inputs.
Typed Decision Models on Tabular Data The paper compares typed probabilistic decision models with large language models on tabular data classification and format robustness. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Evaluates tabular classification models rather than agent planning or tool use.
Typed Decision Models on Tabular Data The paper compares typed probabilistic decision models with large language models on tabular data classification and format robustness. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Studies tabular prompt formatting without retrieval from outside databases.
Typed Decision Models on Tabular Data The paper compares typed probabilistic decision models with large language models on tabular data classification and format robustness. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on decision formatting robustness rather than model compression techniques.
LabCompiler: A Framework for Verifiable Human-Machine Laboratory Execution from Imperfect Protocols The paper introduces a compiler framework that transforms defective natural-language protocols into verifiable laboratory execution workflows. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a domain recovery test rather than a general LLM evaluation benchmark.
Evidence-Anchored Overdose Death Prediction from Death Investigation Reports The paper presents an automated framework to predict suspected drug overdose deaths from investigation reports using expert-guided rationales. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies clinical domain classification rather than general AI safety or guardrails.
Evidence-Anchored Overdose Death Prediction from Death Investigation Reports The paper presents an automated framework to predict suspected drug overdose deaths from investigation reports using expert-guided rationales. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Describes a predictive text classification pipeline rather than autonomous agents.
Evidence-Anchored Overdose Death Prediction from Death Investigation Reports The paper presents an automated framework to predict suspected drug overdose deaths from investigation reports using expert-guided rationales. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Applies NLP to clinical death investigation reports, not software code.
Evidence-Anchored Overdose Death Prediction from Death Investigation Reports The paper presents an automated framework to predict suspected drug overdose deaths from investigation reports using expert-guided rationales. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Classifies internal report text directly without external retrieval augmentation.
Evidence-Anchored Overdose Death Prediction from Death Investigation Reports The paper presents an automated framework to predict suspected drug overdose deaths from investigation reports using expert-guided rationales. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Applies NLP to a specific domain task rather than LLM benchmarking methods.
Evidence-Anchored Overdose Death Prediction from Death Investigation Reports The paper presents an automated framework to predict suspected drug overdose deaths from investigation reports using expert-guided rationales. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model efficiency, distillation, or quantization.
Evidence-Anchored Overdose Death Prediction from Death Investigation Reports The paper presents an automated framework to predict suspected drug overdose deaths from investigation reports using expert-guided rationales. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Processes text investigation reports without computer vision or multimodal inputs.
TEX-MEX: Similarity-Guided Temporal Contrastive Learning for Time Series The paper presents a contrastive learning method for time series that unifies morphological similarity and temporal proximity. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Addresses self-supervised time series learning rather than LLM inference optimization.
TEX-MEX: Similarity-Guided Temporal Contrastive Learning for Time Series The paper presents a contrastive learning method for time series that unifies morphological similarity and temporal proximity. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Does not evaluate large language models or LLM benchmarks.
TEX-MEX: Similarity-Guided Temporal Contrastive Learning for Time Series The paper presents a contrastive learning method for time series that unifies morphological similarity and temporal proximity. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on biological sensor time series, not language model agents.
TEX-MEX: Similarity-Guided Temporal Contrastive Learning for Time Series The paper presents a contrastive learning method for time series that unifies morphological similarity and temporal proximity. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not touch generative AI safety, robustness, or deployment guardrails.
$τ$-Emotion-Bench: Exposing the Interaction Fragility of LLM Agents through Real-World User Emotions The paper introduces a benchmark to measure how tool-calling agents perform when interacting with emotionally varied, impatient users. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent evaluation and training rather than model compression or efficiency.
$τ$-Emotion-Bench: Exposing the Interaction Fragility of LLM Agents through Real-World User Emotions The paper introduces a benchmark to measure how tool-calling agents perform when interacting with emotionally varied, impatient users. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Covers text-based interactive dialog and tool use, not multimodal reasoning.
$τ$-Emotion-Bench: Exposing the Interaction Fragility of LLM Agents through Real-World User Emotions The paper introduces a benchmark to measure how tool-calling agents perform when interacting with emotionally varied, impatient users. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluates tool-use agents in general dialog domains, not software code generation.
$τ$-Emotion-Bench: Exposing the Interaction Fragility of LLM Agents through Real-World User Emotions The paper introduces a benchmark to measure how tool-calling agents perform when interacting with emotionally varied, impatient users. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on user interaction fragility rather than retrieval-augmented generation systems.
REWIND: Uncertainty-Guided Trajectory Repair for Test-Time Reasoning The paper introduces a training-free controller that uses token entropy to target and regenerate faulty reasoning steps at test time. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluates math and general reasoning rather than software engineering or code generation.
REWIND: Uncertainty-Guided Trajectory Repair for Test-Time Reasoning The paper introduces a training-free controller that uses token entropy to target and regenerate faulty reasoning steps at test time. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an inference-time reasoning algorithm rather than benchmark creation or evaluation.
REWIND: Uncertainty-Guided Trajectory Repair for Test-Time Reasoning The paper introduces a training-free controller that uses token entropy to target and regenerate faulty reasoning steps at test time. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, quantization, or lightweight model design.
REWIND: Uncertainty-Guided Trajectory Repair for Test-Time Reasoning The paper introduces a training-free controller that uses token entropy to target and regenerate faulty reasoning steps at test time. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on token-level reasoning repair during text generation rather than autonomous agents.
REWIND: Uncertainty-Guided Trajectory Repair for Test-Time Reasoning The paper introduces a training-free controller that uses token entropy to target and regenerate faulty reasoning steps at test time. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses entirely on text-based mathematical and reasoning benchmarks.
REWIND: Uncertainty-Guided Trajectory Repair for Test-Time Reasoning The paper introduces a training-free controller that uses token entropy to target and regenerate faulty reasoning steps at test time. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve external retrieval or document grounding.
Frame Evidence:A Benchmark for Reference Frames and Visual Evidence in Multi-Image Spatial Reasoning The paper introduces a spatial reasoning benchmark to evaluate how models handle varying reference frames and altered visual evidence. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Tests visual spatial reasoning rather than industrial inspection or defect detection.
Frame Evidence:A Benchmark for Reference Frames and Visual Evidence in Multi-Image Spatial Reasoning The paper introduces a spatial reasoning benchmark to evaluate how models handle varying reference frames and altered visual evidence. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on spatial reasoning accuracy rather than safety or reliability guardrails.
Frame Evidence:A Benchmark for Reference Frames and Visual Evidence in Multi-Image Spatial Reasoning The paper introduces a spatial reasoning benchmark to evaluate how models handle varying reference frames and altered visual evidence. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on static visual question answering benchmarks, not multi-step agent behavior.
Divergence-Routed On-Policy Distillation for Language Models The paper proposes divergence-routed on-policy distillation to target critical token positions where students diverge from teacher language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Investigates distillation loss functions rather than AI deployment safety or guardrails.
Divergence-Routed On-Policy Distillation for Language Models The paper proposes divergence-routed on-policy distillation to target critical token positions where students diverge from teacher language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates distillation on existing math benchmarks rather than proposing evaluation methods.
Divergence-Routed On-Policy Distillation for Language Models The paper proposes divergence-routed on-policy distillation to target critical token positions where students diverge from teacher language models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on policy distillation algorithms rather than agent tool use or planning.
Learning Across Temporal Graph Representation Silos The paper studies transferring learned predictive structure between continuous-time dynamic graphs and temporal knowledge graphs across distinct representations. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Investigates graph pretraining transfer rather than LLM efficiency or distillation.
Learning Across Temporal Graph Representation Silos The paper studies transferring learned predictive structure between continuous-time dynamic graphs and temporal knowledge graphs across distinct representations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Does not address language model evaluation or benchmarks.
Learning Across Temporal Graph Representation Silos The paper studies transferring learned predictive structure between continuous-time dynamic graphs and temporal knowledge graphs across distinct representations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers graph representations rather than generative AI safety or reliability.
Learning Across Temporal Graph Representation Silos The paper studies transferring learned predictive structure between continuous-time dynamic graphs and temporal knowledge graphs across distinct representations. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on dynamic graph representations, not language model agents.
XferAgent: Cross-Agent Experience Transfer for Self-Improving LLM Agents The paper introduces an experience-graph framework enabling heterogeneous agents to share and reuse execution history across diverse tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on experience reuse mechanisms rather than benchmarking or evaluation methodology.
XferAgent: Cross-Agent Experience Transfer for Self-Improving LLM Agents The paper introduces an experience-graph framework enabling heterogeneous agents to share and reuse execution history across diverse tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with performance improvement via memory graphs rather than safety or robustness.
Who's Behind the Text? Robust Model Authorship Attribution The paper proposes a likelihood-based attribution method to identify the source model of machine-generated text under distribution shifts. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address small models, distillation, or inference optimization.
Who's Behind the Text? Robust Model Authorship Attribution The paper proposes a likelihood-based attribution method to identify the source model of machine-generated text under distribution shifts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an attribution algorithm rather than an LLM evaluation benchmark.
Who's Behind the Text? Robust Model Authorship Attribution The paper proposes a likelihood-based attribution method to identify the source model of machine-generated text under distribution shifts. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on model authorship attribution of text, not agent planning or tool use.
Who's Behind the Text? Robust Model Authorship Attribution The paper proposes a likelihood-based attribution method to identify the source model of machine-generated text under distribution shifts. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Addresses general text authorship attribution rather than code generation or repair.
Who's Behind the Text? Robust Model Authorship Attribution The paper proposes a likelihood-based attribution method to identify the source model of machine-generated text under distribution shifts. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses exclusively on text attribution without multimodal capabilities.
Who's Behind the Text? Robust Model Authorship Attribution The paper proposes a likelihood-based attribution method to identify the source model of machine-generated text under distribution shifts. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not investigate retrieval-augmented generation or external document grounding.
JockeyBench: Evaluating LLM Agents on Horse-Racing Decision-Making The paper introduces a horse-racing decision benchmark to evaluate whether agents can extract profitable signals from noisy historical data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Examines betting decision evaluation rather than deployment safety, jailbreaks, or guardrails.
L3M: A Verifiable Harness and Runtime for Self-Extending and Self-Improving Agents The paper presents a Lean 4 runtime allowing self-extending agents to run verified computations across tools without constant human oversight. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Addresses system runtime engineering rather than model compression or inference efficiency.
L3M: A Verifiable Harness and Runtime for Self-Extending and Self-Improving Agents The paper presents a Lean 4 runtime allowing self-extending agents to run verified computations across tools without constant human oversight. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on system runtime architecture rather than LLM evaluation benchmarks.
JevCoder: When Does Decision Delegation Pay Off in Code Repair? The paper evaluates delegating code-repair decisions to low-cost probabilistic judges to reduce token costs without losing accuracy. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Applies existing SWE-bench benchmarks rather than developing new evaluation methodologies.
JevCoder: When Does Decision Delegation Pay Off in Code Repair? The paper evaluates delegating code-repair decisions to low-cost probabilistic judges to reduce token costs without losing accuracy. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent cost and efficiency rather than safety or alignment.
Beyond Static Positional Encoding: Bayesian Time-aware Attention for Non-Stationary Long-term Time Series Forecasting The paper proposes a Bayesian formulation of self-attention to address non-stationary temporal dependencies in long-term time series forecasting. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address AI safety, jailbreaks, or deployment guardrails.
Beyond Static Positional Encoding: Bayesian Time-aware Attention for Non-Stationary Long-term Time Series Forecasting The paper proposes a Bayesian formulation of self-attention to address non-stationary temporal dependencies in long-term time series forecasting. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on forecasting accuracy rather than model compression, quantization, or serving efficiency.
Beyond Static Positional Encoding: Bayesian Time-aware Attention for Non-Stationary Long-term Time Series Forecasting The paper proposes a Bayesian formulation of self-attention to address non-stationary temporal dependencies in long-term time series forecasting. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an architectural mechanism rather than LLM evaluation or benchmarks.
Beyond Static Positional Encoding: Bayesian Time-aware Attention for Non-Stationary Long-term Time Series Forecasting The paper proposes a Bayesian formulation of self-attention to address non-stationary temporal dependencies in long-term time series forecasting. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on time series forecasting transformers, not language model agents.
Divergent Paths: Learning an Outcome-Predictive Representation of LLM Agent Trajectories The paper learns trajectory embeddings to predict the success or failure of language model agents early during execution. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on outcome prediction and training efficiency, not safety or guardrails.
Divergent Paths: Learning an Outcome-Predictive Representation of LLM Agent Trajectories The paper learns trajectory embeddings to predict the success or failure of language model agents early during execution. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Does not address multimodal models or vision-language reasoning.
Divergent Paths: Learning an Outcome-Predictive Representation of LLM Agent Trajectories The paper learns trajectory embeddings to predict the success or failure of language model agents early during execution. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Addresses general agent trajectories rather than retrieval-augmented generation systems.
Divergent Paths: Learning an Outcome-Predictive Representation of LLM Agent Trajectories The paper learns trajectory embeddings to predict the success or failure of language model agents early during execution. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Studies agent trajectories generally rather than software engineering or code generation.
Instruction-Conditioned Motion Control for Robotic Arms: Language-to-Code Decomposition and Reinforcement Learning The paper separates parameter specification, instruction generation, and code synthesis for language-conditioned robotic arm control using reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Discusses reward discrepancies in RL logs rather than deployment safety guardrails.
Instruction-Conditioned Motion Control for Robotic Arms: Language-to-Code Decomposition and Reinforcement Learning The paper separates parameter specification, instruction generation, and code synthesis for language-conditioned robotic arm control using reinforcement learning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotic manipulation and code generation, not industrial visual inspection.
Instruction-Conditioned Motion Control for Robotic Arms: Language-to-Code Decomposition and Reinforcement Learning The paper separates parameter specification, instruction generation, and code synthesis for language-conditioned robotic arm control using reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotic RL control rather than general LLM benchmarking methods.

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