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
Of Errors and Echoes: Responsibility-Aware Credit Assignment for Tool-Use Agents The paper introduces an optimization method that separates error identification from credit assignment to improve reinforcement learning for tool-using agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies RL credit assignment rather than model distillation or quantization.
Of Errors and Echoes: Responsibility-Aware Credit Assignment for Tool-Use Agents The paper introduces an optimization method that separates error identification from credit assignment to improve reinforcement learning for tool-using agents. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Does not involve vision, video, or multimodal processing.
Of Errors and Echoes: Responsibility-Aware Credit Assignment for Tool-Use Agents The paper introduces an optimization method that separates error identification from credit assignment to improve reinforcement learning for tool-using agents. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent tool-use error attribution rather than RAG systems.
Of Errors and Echoes: Responsibility-Aware Credit Assignment for Tool-Use Agents The paper introduces an optimization method that separates error identification from credit assignment to improve reinforcement learning for tool-using agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent credit assignment optimization rather than benchmark evaluation.
OT-FlowRec: Optimal Transport-Guided Multi-Bridge Flow for Multimodal Sequential Recommendation The paper presents an optimal transport flow-matching framework to model concurrent, multi-interest user behavior in multimodal sequential recommendation systems. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on multi-interest recommendation matching rather than text-grounded RAG architectures.
OT-FlowRec: Optimal Transport-Guided Multi-Bridge Flow for Multimodal Sequential Recommendation The paper presents an optimal transport flow-matching framework to model concurrent, multi-interest user behavior in multimodal sequential recommendation systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Concerns recommender system flows rather than language model evaluation frameworks.
OT-FlowRec: Optimal Transport-Guided Multi-Bridge Flow for Multimodal Sequential Recommendation The paper presents an optimal transport flow-matching framework to model concurrent, multi-interest user behavior in multimodal sequential recommendation systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on e-commerce recommendation modeling rather than deployment safety guardrails.
OT-FlowRec: Optimal Transport-Guided Multi-Bridge Flow for Multimodal Sequential Recommendation The paper presents an optimal transport flow-matching framework to model concurrent, multi-interest user behavior in multimodal sequential recommendation systems. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers sequential e-commerce recommendation, not autonomous LLM agents.
When Agents Talk, Secrets Travel: Topology-Driven Leakage and Recipient-Conditioned Sanitization in Multi-Agent LLM Systems The paper investigates privacy leakage caused by multi-agent network topologies and proposes an execution sanitizer conditioned on recipient identity. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on data leakage sanitization rather than benchmark validity testing.
When Agents Talk, Secrets Travel: Topology-Driven Leakage and Recipient-Conditioned Sanitization in Multi-Agent LLM Systems The paper investigates privacy leakage caused by multi-agent network topologies and proposes an execution sanitizer conditioned on recipient identity. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Examines multi-agent privacy topologies, not software engineering code generation.
When Agents Talk, Secrets Travel: Topology-Driven Leakage and Recipient-Conditioned Sanitization in Multi-Agent LLM Systems The paper investigates privacy leakage caused by multi-agent network topologies and proposes an execution sanitizer conditioned on recipient identity. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or efficient serving.
When Agents Talk, Secrets Travel: Topology-Driven Leakage and Recipient-Conditioned Sanitization in Multi-Agent LLM Systems The paper investigates privacy leakage caused by multi-agent network topologies and proposes an execution sanitizer conditioned on recipient identity. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Addresses inter-agent communications rather than RAG indexing or vector retrieval.
When Agents Talk, Secrets Travel: Topology-Driven Leakage and Recipient-Conditioned Sanitization in Multi-Agent LLM Systems The paper investigates privacy leakage caused by multi-agent network topologies and proposes an execution sanitizer conditioned on recipient identity. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Studies textual agent communications rather than vision-language multimodal models.
Autoregressive Decoding as a Martingale: Characterizing the Stochastic Dynamics of LLM Generation The paper models autoregressive decoding errors as a martingale process to analyze the stochastic dynamics of language model generation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on theoretical generation uncertainty rather than model compression or quantization.
Autoregressive Decoding as a Martingale: Characterizing the Stochastic Dynamics of LLM Generation The paper models autoregressive decoding errors as a martingale process to analyze the stochastic dynamics of language model generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes martingale decoding dynamics, not benchmark design or contamination.
Autoregressive Decoding as a Martingale: Characterizing the Stochastic Dynamics of LLM Generation The paper models autoregressive decoding errors as a martingale process to analyze the stochastic dynamics of language model generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on theoretical generation stochasticity rather than deployment safety guardrails.
Autoregressive Decoding as a Martingale: Characterizing the Stochastic Dynamics of LLM Generation The paper models autoregressive decoding errors as a martingale process to analyze the stochastic dynamics of language model generation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies probabilistic decoding theory, not agentic planning or tool use.
AI Representations of Mental Health Stigma: Quantifying the Gap Between AI Estimations and Human Perceptions Across Gender Groups Using GPT-Based LLMs The paper compares mental health stigma representations in large language models against human survey data across different gender groups. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Assesses psychological bias in text prompts, not agentic workflow execution.
AI Representations of Mental Health Stigma: Quantifying the Gap Between AI Estimations and Human Perceptions Across Gender Groups Using GPT-Based LLMs The paper compares mental health stigma representations in large language models against human survey data across different gender groups. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Measures gender stigma representation rather than general LLM benchmarking methods.
Adaptive Compression and Targeted Perturbation: A Unified Framework for Generalized Audio Deepfake Detection The paper introduces an audio deepfake detection framework that dynamically selects latent dimensions and uses spectrogram perturbations to improve generalization across attacks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Targets audio spoofing detection rather than LLM benchmarking methods.
Adaptive Compression and Targeted Perturbation: A Unified Framework for Generalized Audio Deepfake Detection The paper introduces an audio deepfake detection framework that dynamically selects latent dimensions and uses spectrogram perturbations to improve generalization across attacks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers audio deepfake detection, not LLM autonomous agents.
Cross-Protocol Personality Assessment with LLMs: Aligning Behavioral Evidence with Human Ratings The paper proposes a framework to transfer personality assessment across distinct asynchronous video interview protocols using construct alignment and language model raters. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Uses LLM raters for interview scoring, not agentic planning systems.
Cross-Protocol Personality Assessment with LLMs: Aligning Behavioral Evidence with Human Ratings The paper proposes a framework to transfer personality assessment across distinct asynchronous video interview protocols using construct alignment and language model raters. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses recruitment personality psychometrics, not AI deployment safety or guardrails.
Cross-Protocol Personality Assessment with LLMs: Aligning Behavioral Evidence with Human Ratings The paper proposes a framework to transfer personality assessment across distinct asynchronous video interview protocols using construct alignment and language model raters. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies cross-protocol personality scoring rather than foundational LLM evaluation standards.
When Agent Evaluation Becomes Unreliable: An Empirical Study of Oracle Failures and Configuration-Dependent Measurement in an LLM Agent Benchmark The paper audits agent evaluation benchmarks for software defects, showing that broken reward functions and configuration shifts substantially distort reported success rates. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent test harness integrity rather than code synthesis assistants.
Understanding and Reducing Misalignment in Persuasion Optimization The paper studies how reinforcement learning for persuasion causes models to fabricate evidence and coercive tactics, evaluating strategies to reduce this behavior. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies persuasion game dynamics rather than multi-step tool-use agents.
Understanding and Reducing Misalignment in Persuasion Optimization The paper studies how reinforcement learning for persuasion causes models to fabricate evidence and coercive tactics, evaluating strategies to reduce this behavior. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates persuasion alignment dynamics rather than general LLM benchmark reliability.
Understanding and Reducing Misalignment in Persuasion Optimization The paper studies how reinforcement learning for persuasion causes models to fabricate evidence and coercive tactics, evaluating strategies to reduce this behavior. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL alignment trade-offs, not model efficiency or quantization.
Dream2Discern: Learning from Hard Latent Variants for Runtime Failure Prediction in Multi-Agent Systems The paper presents a runtime auditor that predicts execution failures in multi-agent systems using loss-guided perturbations to identify hard failure cases. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not focus on external document retrieval or RAG architectures.
Dream2Discern: Learning from Hard Latent Variants for Runtime Failure Prediction in Multi-Agent Systems The paper presents a runtime auditor that predicts execution failures in multi-agent systems using loss-guided perturbations to identify hard failure cases. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on failure auditing rather than model compression or inference efficiency.
Dream2Discern: Learning from Hard Latent Variants for Runtime Failure Prediction in Multi-Agent Systems The paper presents a runtime auditor that predicts execution failures in multi-agent systems using loss-guided perturbations to identify hard failure cases. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Audits multi-agent execution prefixes without multimodal image or video integration.
Dream2Discern: Learning from Hard Latent Variants for Runtime Failure Prediction in Multi-Agent Systems The paper presents a runtime auditor that predicts execution failures in multi-agent systems using loss-guided perturbations to identify hard failure cases. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a runtime auditor model rather than benchmark measurement methodology.
Dream2Discern: Learning from Hard Latent Variants for Runtime Failure Prediction in Multi-Agent Systems The paper presents a runtime auditor that predicts execution failures in multi-agent systems using loss-guided perturbations to identify hard failure cases. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Targets multi-agent runtime auditing, not software engineering code generation.
Beyond Single-Pathogen Forecasts: Predicting Shared Respiratory Hospital Burden from Influenza, COVID-19, and RSV This paper evaluates methods for forecasting total hospital respiratory burden by comparing direct combined prediction against summing individual pathogen models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies hospital burden forecasting metrics, not language model benchmark methodologies.
Beyond Single-Pathogen Forecasts: Predicting Shared Respiratory Hospital Burden from Influenza, COVID-19, and RSV This paper evaluates methods for forecasting total hospital respiratory burden by comparing direct combined prediction against summing individual pathogen models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses hospital forecasting models, not small model distillation or efficiency.
Beyond Single-Pathogen Forecasts: Predicting Shared Respiratory Hospital Burden from Influenza, COVID-19, and RSV This paper evaluates methods for forecasting total hospital respiratory burden by comparing direct combined prediction against summing individual pathogen models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Concerns public health forecasting rather than AI deployment safety risks.
Beyond Single-Pathogen Forecasts: Predicting Shared Respiratory Hospital Burden from Influenza, COVID-19, and RSV This paper evaluates methods for forecasting total hospital respiratory burden by comparing direct combined prediction against summing individual pathogen models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on epidemiological time-series forecasting, not LLM agents.
Heterogeneous Agentic Coevolution This paper proposes a coevolution framework where language model agents and domain foundation models iteratively train and improve each other. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focus is on agent co-training, not LLM benchmark evaluation.
Heterogeneous Agentic Coevolution This paper proposes a coevolution framework where language model agents and domain foundation models iteratively train and improve each other. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with coevolutionary training rather than safety or reliability guardrails.
The Sharp Directions Are Against You: Curvature Analysis of Activation Steering This paper uses loss curvature to identify and remove fragile directions that cause activation steering vectors to fail. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on internal representation analysis rather than standard benchmarks.
The Sharp Directions Are Against You: Curvature Analysis of Activation Steering This paper uses loss curvature to identify and remove fragile directions that cause activation steering vectors to fail. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies activation steering geometry rather than agentic planning.
Where Polanyi Meets Galois: Tacit Knowing as an Algebro-Deterministic Operation, Toward Re-rooting AI-Generated Science This paper models tacit versus explicit knowledge in scientific discovery as distinct operational regimes in content-addressable memory hardware. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Theoretical memory substrate paper, not deployment AI safety.
Where Polanyi Meets Galois: Tacit Knowing as an Algebro-Deterministic Operation, Toward Re-rooting AI-Generated Science This paper models tacit versus explicit knowledge in scientific discovery as distinct operational regimes in content-addressable memory hardware. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents a hyperdimensional memory hardware concept, not LLM agents.
Where Polanyi Meets Galois: Tacit Knowing as an Algebro-Deterministic Operation, Toward Re-rooting AI-Generated Science This paper models tacit versus explicit knowledge in scientific discovery as distinct operational regimes in content-addressable memory hardware. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Hardware architecture theory, not an LLM evaluation benchmark.
GUIDE: Computationally Augmented Consensus for Trustworthy Clinical Practice Guidelines This paper introduces GUIDE, a multi-agent copilot system that assists clinicians in drafting and updating evidence-based medical practice guidelines. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, distillation, or quantization.
GUIDE: Computationally Augmented Consensus for Trustworthy Clinical Practice Guidelines This paper introduces GUIDE, a multi-agent copilot system that assists clinicians in drafting and updating evidence-based medical practice guidelines. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to coding assistants or program repair.
GUIDE: Computationally Augmented Consensus for Trustworthy Clinical Practice Guidelines This paper introduces GUIDE, a multi-agent copilot system that assists clinicians in drafting and updating evidence-based medical practice guidelines. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical literature text, not multimodal models.
GUIDE: Computationally Augmented Consensus for Trustworthy Clinical Practice Guidelines This paper introduces GUIDE, a multi-agent copilot system that assists clinicians in drafting and updating evidence-based medical practice guidelines. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a clinical system rather than a general LLM benchmark.
Constructed regional photovoltaic generation fields improve ultra-short-term distributed solar forecasting This paper develops SatPV-Field to forecast distributed solar generation by converting fleet measurements into spatial fields with satellite guidance. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on solar fleet forecasting, not LLM agents.
Constructed regional photovoltaic generation fields improve ultra-short-term distributed solar forecasting This paper develops SatPV-Field to forecast distributed solar generation by converting fleet measurements into spatial fields with satellite guidance. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Application-specific forecasting framework, not an LLM benchmark.
Constructed regional photovoltaic generation fields improve ultra-short-term distributed solar forecasting This paper develops SatPV-Field to forecast distributed solar generation by converting fleet measurements into spatial fields with satellite guidance. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on photovoltaic energy modeling, not AI safety.
Imitation Holds a Key to Evolution: Localized Repair and Verified Skill Acquisition for Adaptive Agents This paper presents an imitation framework that enables agents to identify violated contracts, extract skills, and repair execution failures. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an adaptive repair controller rather than an evaluation benchmark.
Towards a Benchmark for Fire Safety Code Interpretation in the Hong Kong AEC Industry This paper evaluates proprietary and open-source language models on Hong Kong fire safety code compliance using expert-curated questions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates question-answering on fire codes rather than agent planning.
PID-Gated Continual Prompt Adaptation Under Noisy Feedback This paper proposes control-inspired gates to decide when language model agents should adapt their prompts without absorbing noisy user feedback. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an adaptation mechanism rather than an evaluation benchmark.
PID-Gated Continual Prompt Adaptation Under Noisy Feedback This paper proposes control-inspired gates to decide when language model agents should adapt their prompts without absorbing noisy user feedback. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not address code generation or software engineering.
PID-Gated Continual Prompt Adaptation Under Noisy Feedback This paper proposes control-inspired gates to decide when language model agents should adapt their prompts without absorbing noisy user feedback. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Studies prompt adaptation for text LLMs, not multimodal models.
PID-Gated Continual Prompt Adaptation Under Noisy Feedback This paper proposes control-inspired gates to decide when language model agents should adapt their prompts without absorbing noisy user feedback. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not involve model pruning, distillation, or quantization.
PID-Gated Continual Prompt Adaptation Under Noisy Feedback This paper proposes control-inspired gates to decide when language model agents should adapt their prompts without absorbing noisy user feedback. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on prompt adaptation scheduling rather than information retrieval.
Query Your Way Out: Benchmarking Multi-Turn Knowledge Reasoning through Generalized Puzzle Solving This paper introduces the Active Probing Benchmark to evaluate multi-turn active questioning and specialized domain reasoning in language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Studies interactive puzzle probing rather than standard RAG systems.
Query Your Way Out: Benchmarking Multi-Turn Knowledge Reasoning through Generalized Puzzle Solving This paper introduces the Active Probing Benchmark to evaluate multi-turn active questioning and specialized domain reasoning in language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on code generation or software engineering.

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