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 Look, Flexible Thought: Learning How and How Long to Think in Multimodal Reasoning The paper proposes a reasoning framework that discretizes visual states into tokens to jointly optimize visual computation and chain-of-thought steps. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on multimodal visual reasoning paths, not retrieval-augmented generation systems.
Adaptive Look, Flexible Thought: Learning How and How Long to Think in Multimodal Reasoning The paper proposes a reasoning framework that discretizes visual states into tokens to jointly optimize visual computation and chain-of-thought steps. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Deals with multimodal chain-of-thought, not code generation or program repair.
Adaptive Look, Flexible Thought: Learning How and How Long to Think in Multimodal Reasoning The paper proposes a reasoning framework that discretizes visual states into tokens to jointly optimize visual computation and chain-of-thought steps. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on general multimodal reasoning rather than industrial visual defect detection.
Adaptive Look, Flexible Thought: Learning How and How Long to Think in Multimodal Reasoning The paper proposes a reasoning framework that discretizes visual states into tokens to jointly optimize visual computation and chain-of-thought steps. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a multimodal reasoning framework rather than general LLM evaluation.
Adaptive Look, Flexible Thought: Learning How and How Long to Think in Multimodal Reasoning The paper proposes a reasoning framework that discretizes visual states into tokens to jointly optimize visual computation and chain-of-thought steps. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Optimizes multimodal reasoning depth, not safety, guardrails, or alignment.
LLM Value Functions for Credit Assignment in Agentic RL The paper uses in-context language model evaluations as state value functions to improve credit assignment in agentic reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on reinforcement learning policy optimization rather than AI safety or guardrails.
LLM Value Functions for Credit Assignment in Agentic RL The paper uses in-context language model evaluations as state value functions to improve credit assignment in agentic reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a reinforcement learning training method rather than an evaluation benchmark.
LLM Value Functions for Credit Assignment in Agentic RL The paper uses in-context language model evaluations as state value functions to improve credit assignment in agentic reinforcement learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Uses LLM value functions for RL training, not model compression or efficiency.
Attention Sinks in VLMs Are Borrowed, Not Seen The paper analyzes visual attention sinks in vision-language models and shows they stem from alignment with language text sinks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies visual attention sinks inside vision-language models, not agent workflows.
Attention Sinks in VLMs Are Borrowed, Not Seen The paper analyzes visual attention sinks in vision-language models and shows they stem from alignment with language text sinks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on internal model attention mechanics rather than evaluation benchmarks.
Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction The paper compares single-head autoregressive beam search with parallel multi-token prediction for generating semantic IDs in recommendation systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Compares two decoding objectives rather than general LLM evaluation benchmarks.
Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction The paper compares single-head autoregressive beam search with parallel multi-token prediction for generating semantic IDs in recommendation systems. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant About item recommendation token generation, not enterprise document retrieval or RAG.
Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction The paper compares single-head autoregressive beam search with parallel multi-token prediction for generating semantic IDs in recommendation systems. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on recommender systems decoding rather than multi-step tool-using agents.
Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction The paper compares single-head autoregressive beam search with parallel multi-token prediction for generating semantic IDs in recommendation systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Evaluates generation latency and ranking, not deployment safety or guardrails.
Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction The paper compares single-head autoregressive beam search with parallel multi-token prediction for generating semantic IDs in recommendation systems. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on discrete semantic ID sequences, not multimodal text-image-video processing.
Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction The paper compares single-head autoregressive beam search with parallel multi-token prediction for generating semantic IDs in recommendation systems. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Deals with recommendation systems, not software code generation or repair.
Engram Injection: A Study of Temporal Validity in Parametric Language-Model Memory The paper studies whether language models can track temporal validity changes in parametric memory via adapter conditioning and clocks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates parametric memory updates, not multi-step agent workflows.
Engram Injection: A Study of Temporal Validity in Parametric Language-Model Memory The paper studies whether language models can track temporal validity changes in parametric memory via adapter conditioning and clocks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates parametric memory updates rather than LLM benchmarking methods.
What the Model Cannot See: Covert and Provable Text Invisibility in LLMs The paper examines how trigger-wrapped text can be made completely invisible to language model activations and downstream tasks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates backdoor attacks in LLM representations, not autonomous planning agents.
What the Model Cannot See: Covert and Provable Text Invisibility in LLMs The paper examines how trigger-wrapped text can be made completely invisible to language model activations and downstream tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on security backdoor attacks rather than benchmark methodology.
Comparative Study of Canvas- and Coordinate-Based Geometry-Conditioned Neural Networks for Real-Time Prediction of Indoor Environments with Varying Room Dimensions The paper compares canvas-based and coordinate-based neural network surrogates for predicting indoor airflow and temperature fields under varying room dimensions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with architectural airflow prediction, not AI safety or alignment.
Comparative Study of Canvas- and Coordinate-Based Geometry-Conditioned Neural Networks for Real-Time Prediction of Indoor Environments with Varying Room Dimensions The paper compares canvas-based and coordinate-based neural network surrogates for predicting indoor airflow and temperature fields under varying room dimensions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Addresses CFD surrogate modeling, not general AI benchmark evaluation.
Comparative Study of Canvas- and Coordinate-Based Geometry-Conditioned Neural Networks for Real-Time Prediction of Indoor Environments with Varying Room Dimensions The paper compares canvas-based and coordinate-based neural network surrogates for predicting indoor airflow and temperature fields under varying room dimensions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on architectural fluid modeling rather than language model compression.
Comparative Study of Canvas- and Coordinate-Based Geometry-Conditioned Neural Networks for Real-Time Prediction of Indoor Environments with Varying Room Dimensions The paper compares canvas-based and coordinate-based neural network surrogates for predicting indoor airflow and temperature fields under varying room dimensions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Uses neural networks for building CFD surrogates, not autonomous agents.
Variance-Optimal State Resampling for Reinforcement Learning with Verifiable Rewards The paper proposes variance-driven state resampling to optimize the state sampling distribution for reinforcement learning with verifiable rewards. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL optimization algorithms rather than benchmark design or testing.
Variance-Optimal State Resampling for Reinforcement Learning with Verifiable Rewards The paper proposes variance-driven state resampling to optimize the state sampling distribution for reinforcement learning with verifiable rewards. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses training sample variance rather than model compression or inference efficiency.
Variance-Optimal State Resampling for Reinforcement Learning with Verifiable Rewards The paper proposes variance-driven state resampling to optimize the state sampling distribution for reinforcement learning with verifiable rewards. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL rollout sampling algorithms rather than language agent architectures.
Variance-Optimal State Resampling for Reinforcement Learning with Verifiable Rewards The paper proposes variance-driven state resampling to optimize the state sampling distribution for reinforcement learning with verifiable rewards. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with RL training efficiency rather than safety, guardrails, or hallucination.
Learning Multimodal Sensor Fusion with JEPA Representations for Breathing Activity and Pose Recognition The paper explores transferring frozen video representations across heterogeneous sensor subsets to classify breathing activity and pose. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates human sensor classification rather than general LLM benchmarking.
Learning Multimodal Sensor Fusion with JEPA Representations for Breathing Activity and Pose Recognition The paper explores transferring frozen video representations across heterogeneous sensor subsets to classify breathing activity and pose. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on sensor fusion classification, not autonomous planning agents.
Learning Multimodal Sensor Fusion with JEPA Representations for Breathing Activity and Pose Recognition The paper explores transferring frozen video representations across heterogeneous sensor subsets to classify breathing activity and pose. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on sensor classification rather than AI safety or alignment risks.
Learning Multimodal Sensor Fusion with JEPA Representations for Breathing Activity and Pose Recognition The paper explores transferring frozen video representations across heterogeneous sensor subsets to classify breathing activity and pose. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on human activity and pose recognition, not industrial inspection.
Make Every Expansion Count: Learning to Search Efficiently in LLM Code Repair The paper introduces a learned search policy that predicts required repair steps to prioritize code edits before generating and testing patches. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on optimizing code search rather than LLM benchmarking methodologies.
Make Every Expansion Count: Learning to Search Efficiently in LLM Code Repair The paper introduces a learned search policy that predicts required repair steps to prioritize code edits before generating and testing patches. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Optimizes search iterations rather than model compression or parameter efficiency.
Make Every Expansion Count: Learning to Search Efficiently in LLM Code Repair The paper introduces a learned search policy that predicts required repair steps to prioritize code edits before generating and testing patches. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on search policies for code repair rather than multi-step tool agents.
Make Every Expansion Count: Learning to Search Efficiently in LLM Code Repair The paper introduces a learned search policy that predicts required repair steps to prioritize code edits before generating and testing patches. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses code repair search efficiency rather than safety or jailbreak prevention.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on pipeline verification accuracy rather than safety risks or jailbreaks.
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.
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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, pruning, or efficient model serving.
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	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Tests general verification pipelines across tasks, not specifically code generation systems.
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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates multi-model pipeline verification, not autonomous multi-step agents.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not evaluate language models or general AI benchmarks.
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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on video generation control rather than model compression or efficiency.
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	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 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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses self-supervised distillation in physics, not LLM agents.
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.
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 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	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	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	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	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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on game theory algorithms, not LLM compression or inference efficiency.

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