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
TRACESTEER: Trace Driven Activation Steering for Hallucination Mitigation in Diffusion Language Models Proposes dynamic activation steering using preceding denoising predictions to reduce hallucinations in diffusion language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Does not involve multi-step agent actions or planning.
SWE-MISBEHAVIOR: Benchmarking and Mitigating Boundary Violations in Software Coding Agents Introduces a benchmark evaluating coding agent misbehaviors across code generation, file management, and system environment integrity. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Does not cover vision, video, or multimodal models.
SWE-MISBEHAVIOR: Benchmarking and Mitigating Boundary Violations in Software Coding Agents Introduces a benchmark evaluating coding agent misbehaviors across code generation, file management, and system environment integrity. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model efficiency, distillation, or serving.
SWE-MISBEHAVIOR: Benchmarking and Mitigating Boundary Violations in Software Coding Agents Introduces a benchmark evaluating coding agent misbehaviors across code generation, file management, and system environment integrity. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation architectures.
PACT: A Pragmatic Prompting Framework for Expressive and Voice-Preserving Speech-to-Speech Translation Presents a pragmatic prompting framework to preserve speaker voice, emotion, and tone during speech-to-speech translation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not propose evaluation benchmarks or benchmark analysis.
PACT: A Pragmatic Prompting Framework for Expressive and Voice-Preserving Speech-to-Speech Translation Presents a pragmatic prompting framework to preserve speaker voice, emotion, and tone during speech-to-speech translation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval or document grounding.
PACT: A Pragmatic Prompting Framework for Expressive and Voice-Preserving Speech-to-Speech Translation Presents a pragmatic prompting framework to preserve speaker voice, emotion, and tone during speech-to-speech translation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model compression, quantization, or serving.
PACT: A Pragmatic Prompting Framework for Expressive and Voice-Preserving Speech-to-Speech Translation Presents a pragmatic prompting framework to preserve speaker voice, emotion, and tone during speech-to-speech translation. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to programming or software engineering.
PACT: A Pragmatic Prompting Framework for Expressive and Voice-Preserving Speech-to-Speech Translation Presents a pragmatic prompting framework to preserve speaker voice, emotion, and tone during speech-to-speech translation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses translation quality and expressiveness, not safety or guardrails.
PACT: A Pragmatic Prompting Framework for Expressive and Voice-Preserving Speech-to-Speech Translation Presents a pragmatic prompting framework to preserve speaker voice, emotion, and tone during speech-to-speech translation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Does not cover planning, tool use, or agent workflows.
Are We Testing Too Much? A Survey of Benchmark Compression for Efficient Evaluation of Large Language Models Surveys methods for compressing large language model benchmarks to reduce evaluation costs while maintaining accurate score estimates. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Does not focus on planning, tools, or agents.
Are We Testing Too Much? A Survey of Benchmark Compression for Efficient Evaluation of Large Language Models Surveys methods for compressing large language model benchmarks to reduce evaluation costs while maintaining accurate score estimates. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on multimodal models or cross-modal tasks.
Are We Testing Too Much? A Survey of Benchmark Compression for Efficient Evaluation of Large Language Models Surveys methods for compressing large language model benchmarks to reduce evaluation costs while maintaining accurate score estimates. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not specific to coding or software engineering tasks.
Are We Testing Too Much? A Survey of Benchmark Compression for Efficient Evaluation of Large Language Models Surveys methods for compressing large language model benchmarks to reduce evaluation costs while maintaining accurate score estimates. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on benchmark dataset compression, not model compression or serving.
Are We Testing Too Much? A Survey of Benchmark Compression for Efficient Evaluation of Large Language Models Surveys methods for compressing large language model benchmarks to reduce evaluation costs while maintaining accurate score estimates. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses evaluation redundancy rather than safety or reliability risks.
Are We Testing Too Much? A Survey of Benchmark Compression for Efficient Evaluation of Large Language Models Surveys methods for compressing large language model benchmarks to reduce evaluation costs while maintaining accurate score estimates. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation.
PAINT: A Framework for Multilingual Pain Representation from Medical Licensing Exams to Real-World Clinical Texts Presents a multilingual framework and dataset spanning four languages to evaluate clinical pain representations across exams and clinical notes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a medical domain dataset rather than general LLM evaluation.
PAINT: A Framework for Multilingual Pain Representation from Medical Licensing Exams to Real-World Clinical Texts Presents a multilingual framework and dataset spanning four languages to evaluate clinical pain representations across exams and clinical notes. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not address code models or software engineering.
PAINT: A Framework for Multilingual Pain Representation from Medical Licensing Exams to Real-World Clinical Texts Presents a multilingual framework and dataset spanning four languages to evaluate clinical pain representations across exams and clinical notes. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses entirely on clinical text without multimodal inputs.
PAINT: A Framework for Multilingual Pain Representation from Medical Licensing Exams to Real-World Clinical Texts Presents a multilingual framework and dataset spanning four languages to evaluate clinical pain representations across exams and clinical notes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses medical information extraction rather than deployment safety risks.
PAINT: A Framework for Multilingual Pain Representation from Medical Licensing Exams to Real-World Clinical Texts Presents a multilingual framework and dataset spanning four languages to evaluate clinical pain representations across exams and clinical notes. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression or inference efficiency.
PAINT: A Framework for Multilingual Pain Representation from Medical Licensing Exams to Real-World Clinical Texts Presents a multilingual framework and dataset spanning four languages to evaluate clinical pain representations across exams and clinical notes. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation techniques.
PAINT: A Framework for Multilingual Pain Representation from Medical Licensing Exams to Real-World Clinical Texts Presents a multilingual framework and dataset spanning four languages to evaluate clinical pain representations across exams and clinical notes. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Does not focus on autonomous multi-step agents or tools.
LegalRewardBench: Benchmarking Reward Models for Legal Preference Evaluation Introduces a human-verified benchmark to evaluate how accurately reward models judge language model outputs on legal tasks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation systems.
LegalRewardBench: Benchmarking Reward Models for Legal Preference Evaluation Introduces a human-verified benchmark to evaluate how accurately reward models judge language model outputs on legal tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not cover model compression or inference efficiency.
LegalRewardBench: Benchmarking Reward Models for Legal Preference Evaluation Introduces a human-verified benchmark to evaluate how accurately reward models judge language model outputs on legal tasks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Does not study autonomous agents or tool use.
LegalRewardBench: Benchmarking Reward Models for Legal Preference Evaluation Introduces a human-verified benchmark to evaluate how accurately reward models judge language model outputs on legal tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on preference evaluation benchmarks rather than deployment safety mechanisms.
LegalRewardBench: Benchmarking Reward Models for Legal Preference Evaluation Introduces a human-verified benchmark to evaluate how accurately reward models judge language model outputs on legal tasks. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses exclusively on text evaluation in the legal domain.
LegalRewardBench: Benchmarking Reward Models for Legal Preference Evaluation Introduces a human-verified benchmark to evaluate how accurately reward models judge language model outputs on legal tasks. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to software engineering or code generation.
Prompt or Policy? Optimizing Physician Slate Presentations with LLM User Simulators Develops a simulation framework using persona-guided models to optimize comparative doctor summary presentations for medical patients. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses content presentation optimization, not safety or guardrails.
Prompt or Policy? Optimizing Physician Slate Presentations with LLM User Simulators Develops a simulation framework using persona-guided models to optimize comparative doctor summary presentations for medical patients. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to code generation or software engineering.
Prompt or Policy? Optimizing Physician Slate Presentations with LLM User Simulators Develops a simulation framework using persona-guided models to optimize comparative doctor summary presentations for medical patients. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not investigate model efficiency, compression, or quantization.
Prompt or Policy? Optimizing Physician Slate Presentations with LLM User Simulators Develops a simulation framework using persona-guided models to optimize comparative doctor summary presentations for medical patients. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Uses LLM judges as a component, but does not benchmark LLMs.
Prompt or Policy? Optimizing Physician Slate Presentations with LLM User Simulators Develops a simulation framework using persona-guided models to optimize comparative doctor summary presentations for medical patients. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not propose novel retrieval-augmented generation methods.
Prompt or Policy? Optimizing Physician Slate Presentations with LLM User Simulators Develops a simulation framework using persona-guided models to optimize comparative doctor summary presentations for medical patients. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals strictly with text-based summaries and profiles.
Prompt or Policy? Optimizing Physician Slate Presentations with LLM User Simulators Develops a simulation framework using persona-guided models to optimize comparative doctor summary presentations for medical patients. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on presentation optimization rather than autonomous agent systems.
Jev-Guard: Fast Routing and Auditing Agent Actions over Editable Policy Trees Introduces a low-latency guardrail model that routes incoming agent actions through editable policy trees for safety auditing. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not evaluate code generation or software engineering assistants.
Jev-Guard: Fast Routing and Auditing Agent Actions over Editable Policy Trees Introduces a low-latency guardrail model that routes incoming agent actions through editable policy trees for safety auditing. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Does not involve multimodal models or data.
Jev-Guard: Fast Routing and Auditing Agent Actions over Editable Policy Trees Introduces a low-latency guardrail model that routes incoming agent actions through editable policy trees for safety auditing. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not focus on retrieval-augmented generation techniques.
Jev-Guard: Fast Routing and Auditing Agent Actions over Editable Policy Trees Introduces a low-latency guardrail model that routes incoming agent actions through editable policy trees for safety auditing. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on latency for guardrails, not model compression or efficiency methods.
Jev-Guard: Fast Routing and Auditing Agent Actions over Editable Policy Trees Introduces a low-latency guardrail model that routes incoming agent actions through editable policy trees for safety auditing. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on deploying guardrail routing rather than benchmark design.
Probing the Geometry of Color Representations in Multilingual Language Models Probes layer representations in multilingual language models to study whether internal color geometry aligns with human perception or language syntax. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval or external grounding.
Probing the Geometry of Color Representations in Multilingual Language Models Probes layer representations in multilingual language models to study whether internal color geometry aligns with human perception or language syntax. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not cover model compression, efficiency, or serving.
Probing the Geometry of Color Representations in Multilingual Language Models Probes layer representations in multilingual language models to study whether internal color geometry aligns with human perception or language syntax. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Investigates text-only multilingual language models, not multimodal systems.
Probing the Geometry of Color Representations in Multilingual Language Models Probes layer representations in multilingual language models to study whether internal color geometry aligns with human perception or language syntax. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Explores representation geometry rather than deployment safety or robustness.
Probing the Geometry of Color Representations in Multilingual Language Models Probes layer representations in multilingual language models to study whether internal color geometry aligns with human perception or language syntax. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on representation probing, not benchmarking or evaluation methodology.
Probing the Geometry of Color Representations in Multilingual Language Models Probes layer representations in multilingual language models to study whether internal color geometry aligns with human perception or language syntax. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not evaluate code generation or software engineering.
Probing the Geometry of Color Representations in Multilingual Language Models Probes layer representations in multilingual language models to study whether internal color geometry aligns with human perception or language syntax. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies internal representations rather than agent planning or actions.
Pin It on Anyone: Certified Targeted Displacement of Multi-Bit LLM Watermarks Without the Key Demonstrates an attack method that alters embedded multi-bit text watermarks without knowing the secret generation key. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Does not involve multi-step agent systems or tool use.
Pin It on Anyone: Certified Targeted Displacement of Multi-Bit LLM Watermarks Without the Key Demonstrates an attack method that alters embedded multi-bit text watermarks without knowing the secret generation key. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not address coding tasks or software engineering.
Pin It on Anyone: Certified Targeted Displacement of Multi-Bit LLM Watermarks Without the Key Demonstrates an attack method that alters embedded multi-bit text watermarks without knowing the secret generation key. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation systems.
Pin It on Anyone: Certified Targeted Displacement of Multi-Bit LLM Watermarks Without the Key Demonstrates an attack method that alters embedded multi-bit text watermarks without knowing the secret generation key. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Exclusively covers text watermarking without multimodal data.
Pin It on Anyone: Certified Targeted Displacement of Multi-Bit LLM Watermarks Without the Key Demonstrates an attack method that alters embedded multi-bit text watermarks without knowing the secret generation key. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates watermarks rather than general LLM capabilities or benchmarks.
Pin It on Anyone: Certified Targeted Displacement of Multi-Bit LLM Watermarks Without the Key Demonstrates an attack method that alters embedded multi-bit text watermarks without knowing the secret generation key. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model compression or inference efficiency.
Multi-Stream Residual Computation in Diffusion Language Models Equips diffusion language models with multiple residual streams and adaptive routing to separate visible context from masked token generation. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not focus on software engineering or code generation.
Multi-Stream Residual Computation in Diffusion Language Models Equips diffusion language models with multiple residual streams and adaptive routing to separate visible context from masked token generation. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Covers text diffusion models, not multimodal models.
Multi-Stream Residual Computation in Diffusion Language Models Equips diffusion language models with multiple residual streams and adaptive routing to separate visible context from masked token generation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on efficiency, quantization, or model distillation.
Multi-Stream Residual Computation in Diffusion Language Models Equips diffusion language models with multiple residual streams and adaptive routing to separate visible context from masked token generation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on internal model architecture, not agents or multi-step execution.
Multi-Stream Residual Computation in Diffusion Language Models Equips diffusion language models with multiple residual streams and adaptive routing to separate visible context from masked token generation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve external retrieval or knowledge grounding.
Multi-Stream Residual Computation in Diffusion Language Models Equips diffusion language models with multiple residual streams and adaptive routing to separate visible context from masked token generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Investigates architecture representations, not safety or guardrails.

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