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
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.
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	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	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	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	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	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Targets multi-agent runtime auditing, not software engineering code generation.
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.
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.
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.
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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies activation steering geometry rather than agentic planning.
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.
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.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Theoretical memory substrate paper, not deployment AI safety.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a clinical system rather than a general LLM 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	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical literature text, not multimodal models.
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.
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 agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on solar fleet forecasting, not LLM agents.
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	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	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	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	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on code generation or software engineering.
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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents an interactive reasoning benchmark rather than agent deployment.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on reasoning evaluation rather than deployment safety risks.
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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression or efficient serving.
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	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates language reasoning without multimodal vision or audio components.
Omni-Modal LLMs Know More Than They Say This paper analyzes why omni-modal language models perceive non-textual inputs correctly yet fail to mention them in text outputs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies cross-modal expression rather than safety guardrails.
Omni-Modal LLMs Know More Than They Say This paper analyzes why omni-modal language models perceive non-textual inputs correctly yet fail to mention them in text outputs. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on multimodal token alignment rather than autonomous agents.
Omni-Modal LLMs Know More Than They Say This paper analyzes why omni-modal language models perceive non-textual inputs correctly yet fail to mention them in text outputs. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Studies omni-modal representations, not industrial inspection.
Omni-Modal LLMs Know More Than They Say This paper analyzes why omni-modal language models perceive non-textual inputs correctly yet fail to mention them in text outputs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, distillation, or quantization.
Omni-Modal LLMs Know More Than They Say This paper analyzes why omni-modal language models perceive non-textual inputs correctly yet fail to mention them in text outputs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on alignment methodology rather than evaluation benchmarks.
Omni-Modal LLMs Know More Than They Say This paper analyzes why omni-modal language models perceive non-textual inputs correctly yet fail to mention them in text outputs. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not focus on document retrieval or grounding pipelines.
Omni-Modal LLMs Know More Than They Say This paper analyzes why omni-modal language models perceive non-textual inputs correctly yet fail to mention them in text outputs. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to programming, code generation, or software engineering.
Large Language Model Assisted Transportation Planning through a Differentiable User Equilibrium Layer This paper combines language models with a differentiable traffic equilibrium layer to help non-experts optimize transportation planning problems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on domain-specific transportation optimization rather than AI safety.
Large Language Model Assisted Transportation Planning through a Differentiable User Equilibrium Layer This paper combines language models with a differentiable traffic equilibrium layer to help non-experts optimize transportation planning problems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Describes an optimization application rather than an evaluation benchmark.
Phantom Merge: When Your Large Language Model Agents Pick One but Tell You About Another This paper identifies and mitigates Phantom Merge, an error where language model agents bind retrieved facts to the wrong entity. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on textual agent reasoning, not multimodal data.
Phantom Merge: When Your Large Language Model Agents Pick One but Tell You About Another This paper identifies and mitigates Phantom Merge, an error where language model agents bind retrieved facts to the wrong entity. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on code generation or software engineering tasks.
Phantom Merge: When Your Large Language Model Agents Pick One but Tell You About Another This paper identifies and mitigates Phantom Merge, an error where language model agents bind retrieved facts to the wrong entity. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Deals with agent entity-binding errors rather than core RAG architectures.
Phantom Merge: When Your Large Language Model Agents Pick One but Tell You About Another This paper identifies and mitigates Phantom Merge, an error where language model agents bind retrieved facts to the wrong entity. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model efficiency, pruning, or quantization.
Phantom Merge: When Your Large Language Model Agents Pick One but Tell You About Another This paper identifies and mitigates Phantom Merge, an error where language model agents bind retrieved facts to the wrong entity. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on error analysis and mitigation rather than a benchmark.
Pollard: Governed Semantic Execution Trees for Framework-Independent AI Agent Control and Replay This paper presents Pollard, a framework-independent agent runtime that uses semantic execution trees for deterministic tracking, governance, and replay. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent runtime infrastructure, not evaluation benchmarks.
Unravel: Recovering an Interactive 3D Scene from a Single Image This paper introduces Unravel, a training-free framework that converts a single image into an editable, multi-object 3D scene. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Uses agentic harness narrowly for 3D graphics reconstruction.

9,181–9,240 of 13,754← Previous Next →