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
Review Rarely Beats the Stronger Model: Separating Verification from Capability Transfer in LLM Pipelines The paper shows that LLM review pipelines rarely outperform the stronger model alone due to confirmation bias rather than true verification. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates text reasoning pipelines rather than multimodal vision-language models.
Review Rarely Beats the Stronger Model: Separating Verification from Capability Transfer in LLM Pipelines The paper shows that LLM review pipelines rarely outperform the stronger model alone due to confirmation bias rather than true verification. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Studies answer verification pipelines rather than retrieval-augmented generation systems.
Gen Through the Lens: Camera-Controllable Video Generation via Cross-Coordinate Conditioning Fusion The paper proposes an image-to-video framework combining three coordinate spaces to enable camera-controllable video generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on generative video framing rather than industrial inspection or defects.
Gen Through the Lens: Camera-Controllable Video Generation via Cross-Coordinate Conditioning Fusion The paper proposes an image-to-video framework combining three coordinate spaces to enable camera-controllable video generation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on video generation control rather than model compression or efficiency.
Gen Through the Lens: Camera-Controllable Video Generation via Cross-Coordinate Conditioning Fusion The paper proposes an image-to-video framework combining three coordinate spaces to enable camera-controllable video generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with video synthesis rather than safety, guardrails, or alignment.
Gen Through the Lens: Camera-Controllable Video Generation via Cross-Coordinate Conditioning Fusion The paper proposes an image-to-video framework combining three coordinate spaces to enable camera-controllable video generation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies video generation methods rather than autonomous agent planning.
Gen Through the Lens: Camera-Controllable Video Generation via Cross-Coordinate Conditioning Fusion The paper proposes an image-to-video framework combining three coordinate spaces to enable camera-controllable video generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not evaluate language models or general AI benchmarks.
LURE: a Phase-Labelled Synthetic Corpus of Online-Grooming Exchanges The paper presents a phase-annotated synthetic conversational corpus generated using a retrieval-augmented pipeline to study online grooming of minors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a domain-specific dataset for criminology rather than LLM benchmarking.
LURE: a Phase-Labelled Synthetic Corpus of Online-Grooming Exchanges The paper presents a phase-annotated synthetic conversational corpus generated using a retrieval-augmented pipeline to study online grooming of minors. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Generates a conversational dataset, not an autonomous agent system.
LURE: a Phase-Labelled Synthetic Corpus of Online-Grooming Exchanges The paper presents a phase-annotated synthetic conversational corpus generated using a retrieval-augmented pipeline to study online grooming of minors. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on grooming detection data rather than model safety or deployment guardrails.
RINO: Renormalization Group Distillation with No Labels The paper presents an unsupervised pretraining method that uses the multiscale structure of jet formation for high-energy physics data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on particle physics data rather than AI safety or guardrails.
RINO: Renormalization Group Distillation with No Labels The paper presents an unsupervised pretraining method that uses the multiscale structure of jet formation for high-energy physics data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not address language model benchmarking or evaluation.
RINO: Renormalization Group Distillation with No Labels The paper presents an unsupervised pretraining method that uses the multiscale structure of jet formation for high-energy physics data. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses self-supervised distillation in physics, not LLM agents.
Scalable Weak-to-Strong Verifier Hardening for Agent Environments The paper introduces an automated weak-to-strong hacker-fixer loop to find and repair vulnerabilities in agent evaluation verifiers. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on verifier hardening rather than model compression or inference efficiency.
Multi-Horizon Explainable Queue Forecasting and Resource-Aware Decision Support for Intelligent Border Control Using LightGBM The paper presents a LightGBM architecture for multi-horizon queue forecasting and resource-aware decision support in border control systems. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Uses LightGBM for tabular queue forecasting, not LLM agents.
Multi-Horizon Explainable Queue Forecasting and Resource-Aware Decision Support for Intelligent Border Control Using LightGBM The paper presents a LightGBM architecture for multi-horizon queue forecasting and resource-aware decision support in border control systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with border operations, not generative AI safety or reliability.
Multi-Horizon Explainable Queue Forecasting and Resource-Aware Decision Support for Intelligent Border Control Using LightGBM The paper presents a LightGBM architecture for multi-horizon queue forecasting and resource-aware decision support in border control systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on border queue prediction, not LLM benchmark evaluation.
DiffNash: Finding Nash Equilibrium in High-Dimensional Continuous Action Spaces with Diffusion Policies The paper presents a diffusion-based framework that uses reweighted score matching to compute Nash equilibria in continuous multi-agent action spaces. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies game-theoretic continuous RL, not language model agent systems.
DiffNash: Finding Nash Equilibrium in High-Dimensional Continuous Action Spaces with Diffusion Policies The paper presents a diffusion-based framework that uses reweighted score matching to compute Nash equilibria in continuous multi-agent action spaces. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses continuous game optimization, not AI safety or deployment guardrails.
DiffNash: Finding Nash Equilibrium in High-Dimensional Continuous Action Spaces with Diffusion Policies The paper presents a diffusion-based framework that uses reweighted score matching to compute Nash equilibria in continuous multi-agent action spaces. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not evaluate LLMs or introduce general evaluation frameworks.
DiffNash: Finding Nash Equilibrium in High-Dimensional Continuous Action Spaces with Diffusion Policies The paper presents a diffusion-based framework that uses reweighted score matching to compute Nash equilibria in continuous multi-agent action spaces. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on game theory algorithms, not LLM compression or inference efficiency.
The Anatomy of a Blind Spot: What an LLM Judge Must Be Shown, and How Much The paper tests what context judge models require, showing that omitting source constraints degrades the quality of iterative feedback loops. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates evaluation setups for LLM judges, not autonomous multi-step agents.
The Anatomy of a Blind Spot: What an LLM Judge Must Be Shown, and How Much The paper tests what context judge models require, showing that omitting source constraints degrades the quality of iterative feedback loops. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on general judge evaluation setups, not coding tools.
The Anatomy of a Blind Spot: What an LLM Judge Must Be Shown, and How Much The paper tests what context judge models require, showing that omitting source constraints degrades the quality of iterative feedback loops. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on LLM-as-a-judge evaluation mechanics rather than safety, guardrails, or attacks.
The Anatomy of a Blind Spot: What an LLM Judge Must Be Shown, and How Much The paper tests what context judge models require, showing that omitting source constraints degrades the quality of iterative feedback loops. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model compression, quantization, or efficient serving.
The Anatomy of a Blind Spot: What an LLM Judge Must Be Shown, and How Much The paper tests what context judge models require, showing that omitting source constraints degrades the quality of iterative feedback loops. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Evaluates LLM judge input formats rather than RAG architectures.
The Anatomy of a Blind Spot: What an LLM Judge Must Be Shown, and How Much The paper tests what context judge models require, showing that omitting source constraints degrades the quality of iterative feedback loops. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Studies text evaluation benchmarks rather than multimodal vision-language models.
Vision: "What Don't I Know About User's Preferences?" How Uncertainty Estimation Improves Proactive Assistant Alignment The paper argues that uncertainty estimation in smart assistants is necessary to prevent unsafe actions when user preferences remain ambiguous. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Is a vision paper on uncertainty, not a benchmark or LLM evaluation study.
Hardware-Assisted Multi-Stage Ransomware Detection with Adaptive Machine Learning The paper combines static software scanning with hardware-level microprocessor monitoring and neural architecture search to detect ransomware in real time. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not address LLM evaluation methods or language benchmarks.
Hardware-Assisted Multi-Stage Ransomware Detection with Adaptive Machine Learning The paper combines static software scanning with hardware-level microprocessor monitoring and neural architecture search to detect ransomware in real time. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on hardware-assisted cybersecurity against ransomware, not generative AI safety.
Hardware-Assisted Multi-Stage Ransomware Detection with Adaptive Machine Learning The paper combines static software scanning with hardware-level microprocessor monitoring and neural architecture search to detect ransomware in real time. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on malware detection rather than language model agents or planning.
Hardware-Assisted Multi-Stage Ransomware Detection with Adaptive Machine Learning The paper combines static software scanning with hardware-level microprocessor monitoring and neural architecture search to detect ransomware in real time. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Applies NAS to malware detection rather than efficient serving or compression of LLMs.
ADAPT: Audio-Depth Action Planning with Transition-Aware Rewards for Lights-Out Industrial Navigation The paper introduces a navigation framework and benchmark using depth and spatial audio for obstacle avoidance in unlit industrial environments. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on autonomous navigation algorithms rather than safety guardrails or alignment.
ADAPT: Audio-Depth Action Planning with Transition-Aware Rewards for Lights-Out Industrial Navigation The paper introduces a navigation framework and benchmark using depth and spatial audio for obstacle avoidance in unlit industrial environments. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a benchmark for navigation robotics, not broader language model evaluation.
Unfair and Unsafe: Everyday Bias Triggers Emergent Misalignment The paper finds that fine-tuning language models on polite text containing demographic stereotypes triggers broader safety and truthfulness failures on unrelated tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates safety degradation from fine-tuning rather than benchmark design or judge reliability.
Unfair and Unsafe: Everyday Bias Triggers Emergent Misalignment The paper finds that fine-tuning language models on polite text containing demographic stereotypes triggers broader safety and truthfulness failures on unrelated tasks. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not address code generation, program repair, or software engineering.
Unfair and Unsafe: Everyday Bias Triggers Emergent Misalignment The paper finds that fine-tuning language models on polite text containing demographic stereotypes triggers broader safety and truthfulness failures on unrelated tasks. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Investigates text-only fine-tuning and safety rather than multimodal models.
Unfair and Unsafe: Everyday Bias Triggers Emergent Misalignment The paper finds that fine-tuning language models on polite text containing demographic stereotypes triggers broader safety and truthfulness failures on unrelated tasks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies alignment risks from fine-tuning, not multi-step agent planning or tool use.
Unfair and Unsafe: Everyday Bias Triggers Emergent Misalignment The paper finds that fine-tuning language models on polite text containing demographic stereotypes triggers broader safety and truthfulness failures on unrelated tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Examines safety alignment dynamics, not model compression or efficient serving.
Unfair and Unsafe: Everyday Bias Triggers Emergent Misalignment The paper finds that fine-tuning language models on polite text containing demographic stereotypes triggers broader safety and truthfulness failures on unrelated tasks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not investigate retrieval systems or external document grounding.
Small but Mighty: Test Suites as Improvement Signals for Self-Evolving Agents The paper presents an evolutionary search method that generates small, high-quality test suites to verify code rewrites in self-evolving agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent self-improvement search rather than general evaluation benchmarks.
Small but Mighty: Test Suites as Improvement Signals for Self-Evolving Agents The paper presents an evolutionary search method that generates small, high-quality test suites to verify code rewrites in self-evolving agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not evaluate safety, red teaming, jailbreaks, or deployment risks.
PopGen: Generating Semantically Diverse Populations for Zero-Shot Coordination The paper uses language models to generate semantically diverse agent policies in code to train reinforcement learning cooperators for zero-shot collaboration. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on population-play training for reinforcement learning, not LLM evaluation benchmarks.
PopGen: Generating Semantically Diverse Populations for Zero-Shot Coordination The paper uses language models to generate semantically diverse agent policies in code to train reinforcement learning cooperators for zero-shot collaboration. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies reinforcement learning partner diversity rather than deployment safety guardrails.
Embedding Information in Policy Randomness: Distribution-Preserving Implicit Communication in MDPs The paper presents a coding method that embeds hidden communication into an agent's policy randomness without altering its observable action distribution. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on MDP information theory and channel coding, not LLM agents.
Embedding Information in Policy Randomness: Distribution-Preserving Implicit Communication in MDPs The paper presents a coding method that embeds hidden communication into an agent's policy randomness without altering its observable action distribution. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Theoretical channel coding paper, not an LLM benchmark evaluation study.
Revocation Is Not Complete at Receipt: An Executable Outcome Contract for Distributed Agent-Tool Gateways The paper introduces an executable contract and monitor to verify that delegated authority revocations actually take effect across distributed agent gateways. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Designs distributed systems contracts, not LLM capability benchmarks.
Handoff Curves: Initial Selection, Later Handoff, and Supervision Targets in Multi-turn LLM Routing The paper evaluates model handoff points in multi-turn routing workflows, analyzing trade-offs between initial model selection, mid-task switching, and operational cost. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies routing optimization metrics rather than general LLM evaluation methods.
Handoff Curves: Initial Selection, Later Handoff, and Supervision Targets in Multi-turn LLM Routing The paper evaluates model handoff points in multi-turn routing workflows, analyzing trade-offs between initial model selection, mid-task switching, and operational cost. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Analyzes multi-turn model routing architectures rather than agent planning tools.
Handoff Curves: Initial Selection, Later Handoff, and Supervision Targets in Multi-turn LLM Routing The paper evaluates model handoff points in multi-turn routing workflows, analyzing trade-offs between initial model selection, mid-task switching, and operational cost. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with cost-routing trade-offs rather than safety guardrails or robustness.
Large language model-generated patterns for epidemiological modeling: a multi-benchmark evaluation framework The paper evaluates whether language models can generate accurate behavioral association patterns for epidemiological modeling using external survey and agent-based benchmarks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on epidemiological modeling validity rather than deployment safety guardrails.
Large language model-generated patterns for epidemiological modeling: a multi-benchmark evaluation framework The paper evaluates whether language models can generate accurate behavioral association patterns for epidemiological modeling using external survey and agent-based benchmarks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies synthetic survey outputs for epidemiology, not agent workflows.
AutoSec-RAN: Specification-Grounded LLM Fine-Tuning for Automated Security Analysis of O-RAN The paper fine-tunes a language model on technical telecommunication specifications to automate security incident triage in Open Radio Access Networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Domain-specific security triage benchmark rather than general LLM evaluation.
AutoSec-RAN: Specification-Grounded LLM Fine-Tuning for Automated Security Analysis of O-RAN The paper fine-tunes a language model on technical telecommunication specifications to automate security incident triage in Open Radio Access Networks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on fine-tuning LLMs for telecom triage, not autonomous agents.
Computational Reflective Equilibrium for Agentic AI: A Rawlsian Alignment Scaffold for Governance The paper proposes an ethical governance scaffold based on reflective equilibrium to resolve conflicting instructions and policy rules in agentic workflows. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on governance scaffolding frameworks rather than benchmark testing.
When Does Compensating Inherited Quantization Error Help? Mean-Square Guarantees for Layer-Wise Quantization and Their Limits The paper provides theoretical mean-square error guarantees and bounds for layer-wise quantization error compensation across linear and non-linear networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes post-training quantization error bounds, not LLM benchmark methodologies.
When Does Compensating Inherited Quantization Error Help? Mean-Square Guarantees for Layer-Wise Quantization and Their Limits The paper provides theoretical mean-square error guarantees and bounds for layer-wise quantization error compensation across linear and non-linear networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Theoretical analysis of quantization noise, not operational AI deployment safety.
When Does Compensating Inherited Quantization Error Help? Mean-Square Guarantees for Layer-Wise Quantization and Their Limits The paper provides theoretical mean-square error guarantees and bounds for layer-wise quantization error compensation across linear and non-linear networks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with network quantization theory, not LLM agents or planning.
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	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Evaluates tool-use credit assignment rather than software development assistants.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Targets RL training optimization rather than deployment safety, guardrails, or red-teaming.

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