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
Autonomous Science Bench: Evaluating AI Agents for Autonomous Scientific Discovery The authors introduce a benchmark to evaluate autonomous scientific discovery agents making iterative experimental decisions under fixed budgets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Benchmarks capability and discovery rather than safety guardrails or alignment.
Learning Perturbation-Specific Representations through Conditional Energy Prior for Cellular Response Prediction The authors present an energy-based prior for cellular perturbation models to better distinguish specific therapeutic signals from general biological background noise. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Not focused on model compression, quantization, or efficient inference.
Learning Perturbation-Specific Representations through Conditional Energy Prior for Cellular Response Prediction The authors present an energy-based prior for cellular perturbation models to better distinguish specific therapeutic signals from general biological background noise. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Applies energy priors to cellular response prediction, not LLM agents.
Learning Perturbation-Specific Representations through Conditional Energy Prior for Cellular Response Prediction The authors present an energy-based prior for cellular perturbation models to better distinguish specific therapeutic signals from general biological background noise. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Drug discovery representation learning, not AI deployment safety.
Learning Perturbation-Specific Representations through Conditional Energy Prior for Cellular Response Prediction The authors present an energy-based prior for cellular perturbation models to better distinguish specific therapeutic signals from general biological background noise. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Domain-specific biological predictive model, not an LLM benchmark.
Readout Scaling Shapes Sparse Feature Recovery The authors analyze readout scaling in sparse autoencoders and propose canonicalized coordinates to improve the recovery of underlying generative factors. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Investigates feature recovery in SAEs, not model compression or efficiency.
Readout Scaling Shapes Sparse Feature Recovery The authors analyze readout scaling in sparse autoencoders and propose canonicalized coordinates to improve the recovery of underlying generative factors. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Does not examine multimodal or vision-language models.
Readout Scaling Shapes Sparse Feature Recovery The authors analyze readout scaling in sparse autoencoders and propose canonicalized coordinates to improve the recovery of underlying generative factors. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation.
Readout Scaling Shapes Sparse Feature Recovery The authors analyze readout scaling in sparse autoencoders and propose canonicalized coordinates to improve the recovery of underlying generative factors. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies sparse autoencoder interpretability, not agent workflows.
Readout Scaling Shapes Sparse Feature Recovery The authors analyze readout scaling in sparse autoencoders and propose canonicalized coordinates to improve the recovery of underlying generative factors. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on coding assistants or software engineering.
Readout Scaling Shapes Sparse Feature Recovery The authors analyze readout scaling in sparse autoencoders and propose canonicalized coordinates to improve the recovery of underlying generative factors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Mechanistic interpretability study, not LLM benchmark evaluation.
MemoryVLN: From Ego-Aware Memory Representation to Cross-Context Memory Reuse in Vision-Language Navigation The authors propose a memory-driven vision-language navigation system using temporal compression and geometric-semantic representations to support long-horizon trajectory tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on embodied navigation, not industrial inspection or defect detection.
MemoryVLN: From Ego-Aware Memory Representation to Cross-Context Memory Reuse in Vision-Language Navigation The authors propose a memory-driven vision-language navigation system using temporal compression and geometric-semantic representations to support long-horizon trajectory tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a navigation architecture, not an evaluation framework or benchmark.
MemoryVLN: From Ego-Aware Memory Representation to Cross-Context Memory Reuse in Vision-Language Navigation The authors propose a memory-driven vision-language navigation system using temporal compression and geometric-semantic representations to support long-horizon trajectory tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Not focused on AI safety, alignment, or guardrails.
ReTerm: Recovery Training for Self-Improving Terminal Agents The authors present a training framework and dataset that enables terminal coding agents to self-improve by recovering from execution errors. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Fine-tunes a model rather than advancing model efficiency or compression.
ReTerm: Recovery Training for Self-Improving Terminal Agents The authors present a training framework and dataset that enables terminal coding agents to self-improve by recovering from execution errors. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Centers on agent task performance rather than safety guardrails or alignment.
ReTerm: Recovery Training for Self-Improving Terminal Agents The authors present a training framework and dataset that enables terminal coding agents to self-improve by recovering from execution errors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a training method, not a benchmark or evaluation study.
Task-evoked eye movements as candidate digital biomarkers of depression severity in adolescents and young adults The authors analyze task-evoked eye movements in adolescents to identify objective behavioral markers corresponding to depression severity scores. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Not an evaluation or benchmark of language models.
Task-evoked eye movements as candidate digital biomarkers of depression severity in adolescents and young adults The authors analyze task-evoked eye movements in adolescents to identify objective behavioral markers corresponding to depression severity scores. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Medical depression diagnosis study, not AI deployment safety or reliability.
Task-evoked eye movements as candidate digital biomarkers of depression severity in adolescents and young adults The authors analyze task-evoked eye movements in adolescents to identify objective behavioral markers corresponding to depression severity scores. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Clinical study of eye-movement biomarkers, not LLM agents.
Judges Follow the Reference: The Derivation Gap in Verifying Tool-Call Arguments The authors identify a derivation gap where LLM judges reject correct tool arguments lacking direct textual references, and propose a dedicated reference model. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on code generation or software engineering agents.
Judges Follow the Reference: The Derivation Gap in Verifying Tool-Call Arguments The authors identify a derivation gap where LLM judges reject correct tool arguments lacking direct textual references, and propose a dedicated reference model. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Evaluates tool arguments, not retrieval-augmented generation systems.
Judges Follow the Reference: The Derivation Gap in Verifying Tool-Call Arguments The authors identify a derivation gap where LLM judges reject correct tool arguments lacking direct textual references, and propose a dedicated reference model. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Does not involve multimodal reasoning or vision-language models.
Judges Follow the Reference: The Derivation Gap in Verifying Tool-Call Arguments The authors identify a derivation gap where LLM judges reject correct tool arguments lacking direct textual references, and propose a dedicated reference model. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or efficient serving.
Aligning Faithfulness for Theory-Scale Autoformalization The authors propose evaluating autoformalization faithfulness by aligning phrase-level correspondences between informal mathematics and formal code under contextual interpretations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on mathematical autoformalization accuracy, not deployment safety.
Aligning Faithfulness for Theory-Scale Autoformalization The authors propose evaluating autoformalization faithfulness by aligning phrase-level correspondences between informal mathematics and formal code under contextual interpretations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, pruning, or efficient inference.
Aligning Faithfulness for Theory-Scale Autoformalization The authors propose evaluating autoformalization faithfulness by aligning phrase-level correspondences between informal mathematics and formal code under contextual interpretations. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Does not involve multimodal models or vision-language data.
Aligning Faithfulness for Theory-Scale Autoformalization The authors propose evaluating autoformalization faithfulness by aligning phrase-level correspondences between informal mathematics and formal code under contextual interpretations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on mathematical autoformalization, not LLM agents or tool use.
Aligning Faithfulness for Theory-Scale Autoformalization The authors propose evaluating autoformalization faithfulness by aligning phrase-level correspondences between informal mathematics and formal code under contextual interpretations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not study retrieval-augmented generation systems.
EgoOmniWorld: Unifying Egocentric Understanding via Ego-Action-World Interactions The authors introduce a benchmark evaluating egocentric video understanding structured around an interactive loop of user perception, action, and world feedback. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Deals with egocentric human video, not industrial defect detection.
EgoOmniWorld: Unifying Egocentric Understanding via Ego-Action-World Interactions The authors introduce a benchmark evaluating egocentric video understanding structured around an interactive loop of user perception, action, and world feedback. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Evaluates video interaction QA, not safety risks or guardrails.
EgoOmniWorld: Unifying Egocentric Understanding via Ego-Action-World Interactions The authors introduce a benchmark evaluating egocentric video understanding structured around an interactive loop of user perception, action, and world feedback. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on egocentric video QA, not LLM agentic tool use.
The Smart Buildings Challenge: Learning Foundation Models for Real-World Optimization at Scale The authors introduce a challenge benchmark to develop foundation models for building HVAC energy optimization that transfer across different physical structures. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on HVAC optimization policies, not LLM agent systems.
The Smart Buildings Challenge: Learning Foundation Models for Real-World Optimization at Scale The authors introduce a challenge benchmark to develop foundation models for building HVAC energy optimization that transfer across different physical structures. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a challenge for building HVAC control, not LLM evaluation.
The Smart Buildings Challenge: Learning Foundation Models for Real-World Optimization at Scale The authors introduce a challenge benchmark to develop foundation models for building HVAC energy optimization that transfer across different physical structures. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on HVAC energy efficiency, not LLM deployment safety.
A Global View of How LLMs Value Human Time The paper evaluates how commercial language models value human time by measuring recommended delay compensation amounts across 193 different countries. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates geographical valuation bias in LLMs, not multi-step agent architectures.
The Geometry of Language Model Logits: Generic Structure and Learned Departures The paper analyzes the geometric distribution of language model output logits, identifying a consistent log-linear relationship that emerges during pretraining. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not focus on code generation or software engineering tasks.
The Geometry of Language Model Logits: Generic Structure and Learned Departures The paper analyzes the geometric distribution of language model output logits, identifying a consistent log-linear relationship that emerges during pretraining. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Examines logit distribution geometry, not agent planning or tools.
The Geometry of Language Model Logits: Generic Structure and Learned Departures The paper analyzes the geometric distribution of language model output logits, identifying a consistent log-linear relationship that emerges during pretraining. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates foundational logit geometry rather than evaluation benchmarks.
The Geometry of Language Model Logits: Generic Structure and Learned Departures The paper analyzes the geometric distribution of language model output logits, identifying a consistent log-linear relationship that emerges during pretraining. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Theoretical study of logit rank patterns, not deployment safety.
The Geometry of Language Model Logits: Generic Structure and Learned Departures The paper analyzes the geometric distribution of language model output logits, identifying a consistent log-linear relationship that emerges during pretraining. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Analyzes pretraining logit distributions, not model compression or efficient serving.
The Geometry of Language Model Logits: Generic Structure and Learned Departures The paper analyzes the geometric distribution of language model output logits, identifying a consistent log-linear relationship that emerges during pretraining. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text autoregressive logit geometry, not multimodal models.
The Geometry of Language Model Logits: Generic Structure and Learned Departures The paper analyzes the geometric distribution of language model output logits, identifying a consistent log-linear relationship that emerges during pretraining. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Studies next-token logit distributions, not retrieval augmentation.
Lost in Implicit Routing: How LLM Agents Fail to Load Relevant Skills at Scale The paper shows that large language model agents fail to retrieve relevant skills when large numbers of irrelevant skills populate the metadata library. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies skill-loading failure modes rather than proposing an LLM benchmark.
APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction The paper presents an extended behavior tree framework that automates modular robot task planning and runtime replanning for off-site construction fabrication. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Presents a DSL for construction robotics, not LLMs for code.
APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction The paper presents an extended behavior tree framework that automates modular robot task planning and runtime replanning for off-site construction fabrication. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a robotic execution architecture, not an LLM benchmark.
APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction The paper presents an extended behavior tree framework that automates modular robot task planning and runtime replanning for off-site construction fabrication. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on classical symbolic planners and behavior trees, not LLM agents.
APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction The paper presents an extended behavior tree framework that automates modular robot task planning and runtime replanning for off-site construction fabrication. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on construction robotic planning, not industrial visual inspection.
APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction The paper presents an extended behavior tree framework that automates modular robot task planning and runtime replanning for off-site construction fabrication. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with formal robotic verification, not deployed LLM safety.
On Symmetry for Characterizing Agent Heterogeneity in Multi-Agent Systems The paper defines an algebraic symmetry framework for characterizing structural agent heterogeneity and policy representations in multi-agent systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Mathematical analysis of agent symmetry, not an LLM benchmark.
On Symmetry for Characterizing Agent Heterogeneity in Multi-Agent Systems The paper defines an algebraic symmetry framework for characterizing structural agent heterogeneity and policy representations in multi-agent systems. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical game theory on POSGs, not LLM-based agent systems.
On Symmetry for Characterizing Agent Heterogeneity in Multi-Agent Systems The paper defines an algebraic symmetry framework for characterizing structural agent heterogeneity and policy representations in multi-agent systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address practical deployment safety or LLM reliability.
Input-Conditioned Search for Efficient Test-Time Scaling of Recursive Reasoners The paper presents a test-time search method that perturbs input conditions to generate diverse reasoning paths in recursive reasoning models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on test-time reasoning search, not safety or guardrails.
Input-Conditioned Search for Efficient Test-Time Scaling of Recursive Reasoners The paper presents a test-time search method that perturbs input conditions to generate diverse reasoning paths in recursive reasoning models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on recursive reasoner inference search, not tool-using agents.
Input-Conditioned Search for Efficient Test-Time Scaling of Recursive Reasoners The paper presents a test-time search method that perturbs input conditions to generate diverse reasoning paths in recursive reasoning models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an inference scaling technique, not an evaluation benchmark.
Rethinking Quality in Large Language Model Routing: Security-Conditioned Model-and-Budget Selection The paper proposes a query router that balances answer utility, security risks, and token budgets when selecting language models for mixed enterprise workloads. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on request routing across models, not multi-step LLM agents.
Configuring RAG Systems: What Breaks, Where Originates, and How to Fix The paper presents an empirical study of 654 real-world configuration bugs across ten retrieval-augmented generation systems, categorizing causes and fixes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies software configuration bugs in RAG rather than LLM benchmarking.
Configuring RAG Systems: What Breaks, Where Originates, and How to Fix The paper presents an empirical study of 654 real-world configuration bugs across ten retrieval-augmented generation systems, categorizing causes and fixes. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates RAG configuration issues rather than agentic planning or tools.
Order-Consistent Latent Geometry for Reliable Short-Horizon Planning in World Models The paper analyzes pathwise distance consistency in visual world models to avoid candidate order reversals during latent short-horizon planning. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on world-model latent trajectory geometry, not LLM agent systems.
Order-Consistent Latent Geometry for Reliable Short-Horizon Planning in World Models The paper analyzes pathwise distance consistency in visual world models to avoid candidate order reversals during latent short-horizon planning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Theoretical planning geometry paper, not an LLM benchmark.

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