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
Hyperbolic Time Series Forecasting with Learnable Decomposition and Manifold Dynamics The paper proposes a hyperbolic time series forecasting framework that maps multiscale frequency hierarchies onto the Poincaré ball. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a hyperbolic forecasting architecture, not an LLM evaluation benchmark.
Hyperbolic Time Series Forecasting with Learnable Decomposition and Manifold Dynamics The paper proposes a hyperbolic time series forecasting framework that maps multiscale frequency hierarchies onto the Poincaré ball. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on time series forecasting models, not LLM agents.
A Decade in a Day: Synthesizing Agent-Native Interfaces from Human-Oriented Software Evolution The paper introduces a benchmark measuring how software interface versions affect the task performance of desktop language model agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on interface usability for agents, not safety or guardrails.
SurgiCLAW: An Interpretable Atomic-Skill Agent for Precision Robotic Micromanipulation The paper presents a hierarchical agent combining vision-language models for task planning with deterministic control programs for micromanipulation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Targets robotic execution precision rather than general enterprise AI deployment safety.
SurgiCLAW: An Interpretable Atomic-Skill Agent for Precision Robotic Micromanipulation The paper presents a hierarchical agent combining vision-language models for task planning with deterministic control programs for micromanipulation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on surgical robotic micromanipulation, not industrial manufacturing quality control.
SurgiCLAW: An Interpretable Atomic-Skill Agent for Precision Robotic Micromanipulation The paper presents a hierarchical agent combining vision-language models for task planning with deterministic control programs for micromanipulation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a surgical robotic framework rather than general LLM evaluation.
Agent Sociodrama: Social-Dynamics-Guided Persona Recasting for LLM Agent Teams The paper introduces a framework to model evolving social dynamics and update personas in multi-agent language model teams. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an agent coordination method rather than an evaluation benchmark.
Agent Sociodrama: Social-Dynamics-Guided Persona Recasting for LLM Agent Teams The paper introduces a framework to model evolving social dynamics and update personas in multi-agent language model teams. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Investigates social persona dynamics, not software engineering or code generation.
Agent Sociodrama: Social-Dynamics-Guided Persona Recasting for LLM Agent Teams The paper introduces a framework to model evolving social dynamics and update personas in multi-agent language model teams. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on multi-agent collaboration dynamics, not retrieval-augmented generation.
Agent Sociodrama: Social-Dynamics-Guided Persona Recasting for LLM Agent Teams The paper introduces a framework to model evolving social dynamics and update personas in multi-agent language model teams. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-based persona interactions, not multimodal text-image systems.
Agent Sociodrama: Social-Dynamics-Guided Persona Recasting for LLM Agent Teams The paper introduces a framework to model evolving social dynamics and update personas in multi-agent language model teams. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not explore model compression, quantization, or efficient serving.
Agent Sociodrama: Social-Dynamics-Guided Persona Recasting for LLM Agent Teams The paper introduces a framework to model evolving social dynamics and update personas in multi-agent language model teams. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies persona recasting for performance, not deployment safety or jailbreak prevention.
An Explainable System for Offensive Content Detection in Nastaliq Urdu: Corpus Curation, Transformer Benchmarking, and Word-Level Attribution The paper presents a Nastaliq Urdu comment corpus and evaluates transformer models with word-level attributions for offensive content detection. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Compares standard transformer classifiers without studying model compression or efficiency.
An Explainable System for Offensive Content Detection in Nastaliq Urdu: Corpus Curation, Transformer Benchmarking, and Word-Level Attribution The paper presents a Nastaliq Urdu comment corpus and evaluates transformer models with word-level attributions for offensive content detection. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Concerns natural language moderation, not coding or software engineering.
An Explainable System for Offensive Content Detection in Nastaliq Urdu: Corpus Curation, Transformer Benchmarking, and Word-Level Attribution The paper presents a Nastaliq Urdu comment corpus and evaluates transformer models with word-level attributions for offensive content detection. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on text classification, not retrieval-augmented generation architectures.
An Explainable System for Offensive Content Detection in Nastaliq Urdu: Corpus Curation, Transformer Benchmarking, and Word-Level Attribution The paper presents a Nastaliq Urdu comment corpus and evaluates transformer models with word-level attributions for offensive content detection. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses solely on text classification in Urdu, not multimodal models.
An Explainable System for Offensive Content Detection in Nastaliq Urdu: Corpus Curation, Transformer Benchmarking, and Word-Level Attribution The paper presents a Nastaliq Urdu comment corpus and evaluates transformer models with word-level attributions for offensive content detection. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with content moderation classification, not autonomous multi-step agents.
Passing Tests, Missing Semantics: Evidence of Interpretive Lock-In in Plan-Based DataFrame Code Generation The paper examines interpretive lock-in where planning agents generate code passing self-generated tests while missing the actual specification requirements. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies prompt planning and testing in code generation, not agent architectures.
Passing Tests, Missing Semantics: Evidence of Interpretive Lock-In in Plan-Based DataFrame Code Generation The paper examines interpretive lock-in where planning agents generate code passing self-generated tests while missing the actual specification requirements. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on semantic code correctness rather than safety, guardrails, or jailbreaks.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Optimizes multimodal reasoning depth, not safety, guardrails, or alignment.
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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies multimodal internal reasoning depth rather than autonomous multi-step software agents.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a multimodal reasoning framework rather than general LLM evaluation.
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.
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.
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	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Deals with recommendation systems, not software code generation or repair.
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	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	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	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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Evaluates generation latency and ranking, not deployment safety or guardrails.
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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Uses neural networks for building CFD surrogates, not autonomous agents.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Addresses CFD surrogate modeling, not general AI benchmark evaluation.
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 safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with RL training efficiency rather than safety, guardrails, or hallucination.
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.
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.
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.
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	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	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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates multi-model pipeline verification, not autonomous multi-step agents.
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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, pruning, or efficient model serving.

12,541–12,600 of 17,313← Previous Next →