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
Batch-Paced: Fixed-Budget Batch Decoding in Discrete Diffusion Language Models The paper proposes Batch-Paced, a method that dynamically allocates decoding compute across batched requests to improve discrete diffusion language model inference. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses inference efficiency rather than safety, guardrails, or jailbreak prevention.
Batch-Paced: Fixed-Budget Batch Decoding in Discrete Diffusion Language Models The paper proposes Batch-Paced, a method that dynamically allocates decoding compute across batched requests to improve discrete diffusion language model inference. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on diffusion decoding algorithms rather than multi-step autonomous agent systems.
Batch-Paced: Fixed-Budget Batch Decoding in Discrete Diffusion Language Models The paper proposes Batch-Paced, a method that dynamically allocates decoding compute across batched requests to improve discrete diffusion language model inference. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Studies discrete diffusion language models without multimodal inputs or outputs.
Agent-Reflex: Teaching Language Agents to Act Reflectively The paper introduces Agent-Reflex, a training framework designed to teach language agents how to reflect and recover from noisy environment feedback. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, pruning, or serving efficiency.
Agent-Reflex: Teaching Language Agents to Act Reflectively The paper introduces Agent-Reflex, a training framework designed to teach language agents how to reflect and recover from noisy environment feedback. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not focus on retrieval-augmented generation methods.
Agent-Reflex: Teaching Language Agents to Act Reflectively The paper introduces Agent-Reflex, a training framework designed to teach language agents how to reflect and recover from noisy environment feedback. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not specific to software engineering or code generation.
Agent-Reflex: Teaching Language Agents to Act Reflectively The paper introduces Agent-Reflex, a training framework designed to teach language agents how to reflect and recover from noisy environment feedback. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a training framework rather than evaluation benchmarks.
Agent-Reflex: Teaching Language Agents to Act Reflectively The paper introduces Agent-Reflex, a training framework designed to teach language agents how to reflect and recover from noisy environment feedback. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on language agents without multimodal data.
Fish Audio Drama 3 Technical Report The paper introduces Fish Audio Drama 3, an open-source text-to-speech model designed for multi-character dialogue, long-form synthesis, and iterative voice revision. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not cover software engineering or code generation.
Fish Audio Drama 3 Technical Report The paper introduces Fish Audio Drama 3, an open-source text-to-speech model designed for multi-character dialogue, long-form synthesis, and iterative voice revision. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Presents a large 35B model, not model compression or small models.
Fish Audio Drama 3 Technical Report The paper introduces Fish Audio Drama 3, an open-source text-to-speech model designed for multi-character dialogue, long-form synthesis, and iterative voice revision. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a model technical report rather than evaluation benchmarks.
Fish Audio Drama 3 Technical Report The paper introduces Fish Audio Drama 3, an open-source text-to-speech model designed for multi-character dialogue, long-form synthesis, and iterative voice revision. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not focus on deployment safety, jailbreaks, or guardrails.
Fish Audio Drama 3 Technical Report The paper introduces Fish Audio Drama 3, an open-source text-to-speech model designed for multi-character dialogue, long-form synthesis, and iterative voice revision. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation architectures.
Fish Audio Drama 3 Technical Report The paper introduces Fish Audio Drama 3, an open-source text-to-speech model designed for multi-character dialogue, long-form synthesis, and iterative voice revision. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Does not focus on autonomous planning, agents, or tool use.
Machine--human agreement in marking undergraduate assignments is specific to model, prompt and institution The paper examines machine grading on undergraduate essays across three universities, finding models compress score distributions and vary across institutions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Does not study multi-step agents, planning, or tool use.
Machine--human agreement in marking undergraduate assignments is specific to model, prompt and institution The paper examines machine grading on undergraduate essays across three universities, finding models compress score distributions and vary across institutions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not cover retrieval-augmented generation.
Machine--human agreement in marking undergraduate assignments is specific to model, prompt and institution The paper examines machine grading on undergraduate essays across three universities, finding models compress score distributions and vary across institutions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression or inference efficiency.
Machine--human agreement in marking undergraduate assignments is specific to model, prompt and institution The paper examines machine grading on undergraduate essays across three universities, finding models compress score distributions and vary across institutions. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not address coding or software engineering.
Machine--human agreement in marking undergraduate assignments is specific to model, prompt and institution The paper examines machine grading on undergraduate essays across three universities, finding models compress score distributions and vary across institutions. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Analyzes text essay grading, not multimodal models.
A Benchmark for Bangla Task-Oriented Dialogue Understanding The paper introduces BanglaTOD, a tri-lingual benchmark for task-oriented dialogue understanding covering Bangla, English, and romanized Banglish across nine service domains. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals solely with text dialogue without multimodal data.
A Benchmark for Bangla Task-Oriented Dialogue Understanding The paper introduces BanglaTOD, a tri-lingual benchmark for task-oriented dialogue understanding covering Bangla, English, and romanized Banglish across nine service domains. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on dialogue comprehension, not safety or reliability risks.
A Benchmark for Bangla Task-Oriented Dialogue Understanding The paper introduces BanglaTOD, a tri-lingual benchmark for task-oriented dialogue understanding covering Bangla, English, and romanized Banglish across nine service domains. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not address code generation or software engineering.
A Benchmark for Bangla Task-Oriented Dialogue Understanding The paper introduces BanglaTOD, a tri-lingual benchmark for task-oriented dialogue understanding covering Bangla, English, and romanized Banglish across nine service domains. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not cover model compression, distillation, or efficient inference.
A Benchmark for Bangla Task-Oriented Dialogue Understanding The paper introduces BanglaTOD, a tri-lingual benchmark for task-oriented dialogue understanding covering Bangla, English, and romanized Banglish across nine service domains. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a language-specific dialogue dataset rather than broader LLM evaluation.
A Benchmark for Bangla Task-Oriented Dialogue Understanding The paper introduces BanglaTOD, a tri-lingual benchmark for task-oriented dialogue understanding covering Bangla, English, and romanized Banglish across nine service domains. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation.
A Benchmark for Bangla Task-Oriented Dialogue Understanding The paper introduces BanglaTOD, a tri-lingual benchmark for task-oriented dialogue understanding covering Bangla, English, and romanized Banglish across nine service domains. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on dialogue state tracking rather than autonomous agent planning.
From Agents to Edges: Paired Optimization for Multi-Agent Collaboration The paper introduces an optimization method that jointly refines communication instructions between communicating agents to improve multi-agent collaboration. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation methods.
From Agents to Edges: Paired Optimization for Multi-Agent Collaboration The paper introduces an optimization method that jointly refines communication instructions between communicating agents to improve multi-agent collaboration. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not specific to coding or software engineering agents.
From Agents to Edges: Paired Optimization for Multi-Agent Collaboration The paper introduces an optimization method that jointly refines communication instructions between communicating agents to improve multi-agent collaboration. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on optimization methods rather than benchmark design.
From Agents to Edges: Paired Optimization for Multi-Agent Collaboration The paper introduces an optimization method that jointly refines communication instructions between communicating agents to improve multi-agent collaboration. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on task collaboration performance rather than safety guardrails.
From Agents to Edges: Paired Optimization for Multi-Agent Collaboration The paper introduces an optimization method that jointly refines communication instructions between communicating agents to improve multi-agent collaboration. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-based multi-agent collaboration, not multimodal models.
From Agents to Edges: Paired Optimization for Multi-Agent Collaboration The paper introduces an optimization method that jointly refines communication instructions between communicating agents to improve multi-agent collaboration. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not cover model compression or inference efficiency.
Narrative Lookahead in LLMs: Event-Level Decodability, Predictive Advantage, and Steering The paper probes language model hidden states across generated stories to test whether models anticipate narrative events multiple steps ahead. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not focus on code generation or software engineering.
Narrative Lookahead in LLMs: Event-Level Decodability, Predictive Advantage, and Steering The paper probes language model hidden states across generated stories to test whether models anticipate narrative events multiple steps ahead. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not focus on benchmarking or LLM evaluation frameworks.
Narrative Lookahead in LLMs: Event-Level Decodability, Predictive Advantage, and Steering The paper probes language model hidden states across generated stories to test whether models anticipate narrative events multiple steps ahead. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Exclusively covers text story generation, not multimodal inputs.
Narrative Lookahead in LLMs: Event-Level Decodability, Predictive Advantage, and Steering The paper probes language model hidden states across generated stories to test whether models anticipate narrative events multiple steps ahead. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, distillation, or serving.
Narrative Lookahead in LLMs: Event-Level Decodability, Predictive Advantage, and Steering The paper probes language model hidden states across generated stories to test whether models anticipate narrative events multiple steps ahead. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Probes internal representations rather than deployment safety or jailbreaks.
Narrative Lookahead in LLMs: Event-Level Decodability, Predictive Advantage, and Steering The paper probes language model hidden states across generated stories to test whether models anticipate narrative events multiple steps ahead. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation methods.
Narrative Lookahead in LLMs: Event-Level Decodability, Predictive Advantage, and Steering The paper probes language model hidden states across generated stories to test whether models anticipate narrative events multiple steps ahead. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates text generation dynamics, not multi-step agents or tools.
Cheap, Consistent, Calibrated, but Biased? Typed Decision Models as Judges The paper evaluates typed decision models against standard language model judges to measure how emotional tone and biases affect their evaluation accuracy. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Does not examine agents, planning, or tool use.
Cheap, Consistent, Calibrated, but Biased? Typed Decision Models as Judges The paper evaluates typed decision models against standard language model judges to measure how emotional tone and biases affect their evaluation accuracy. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation systems.
Cheap, Consistent, Calibrated, but Biased? Typed Decision Models as Judges The paper evaluates typed decision models against standard language model judges to measure how emotional tone and biases affect their evaluation accuracy. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies decision model bias, not model compression or efficiency techniques.
Cheap, Consistent, Calibrated, but Biased? Typed Decision Models as Judges The paper evaluates typed decision models against standard language model judges to measure how emotional tone and biases affect their evaluation accuracy. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not cover code models or software engineering.
Cheap, Consistent, Calibrated, but Biased? Typed Decision Models as Judges The paper evaluates typed decision models against standard language model judges to measure how emotional tone and biases affect their evaluation accuracy. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses entirely on text-based decision models.
SparPR: Runtime KV-Cache Recovery for Resource-Constrained LLM Inference on Consumer Hardware The paper presents SparPR, a runtime key-value cache recovery mechanism that reduces memory bottlenecks during model inference on consumer hardware. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on code generation or software engineering.
SparPR: Runtime KV-Cache Recovery for Resource-Constrained LLM Inference on Consumer Hardware The paper presents SparPR, a runtime key-value cache recovery mechanism that reduces memory bottlenecks during model inference on consumer hardware. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on inference hardware optimization, not safety or guardrails.
SparPR: Runtime KV-Cache Recovery for Resource-Constrained LLM Inference on Consumer Hardware The paper presents SparPR, a runtime key-value cache recovery mechanism that reduces memory bottlenecks during model inference on consumer hardware. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Addresses language model inference, not multimodal architectures.
SparPR: Runtime KV-Cache Recovery for Resource-Constrained LLM Inference on Consumer Hardware The paper presents SparPR, a runtime key-value cache recovery mechanism that reduces memory bottlenecks during model inference on consumer hardware. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not introduce or study benchmarks for LLM evaluation.
SparPR: Runtime KV-Cache Recovery for Resource-Constrained LLM Inference on Consumer Hardware The paper presents SparPR, a runtime key-value cache recovery mechanism that reduces memory bottlenecks during model inference on consumer hardware. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation architectures.
SparPR: Runtime KV-Cache Recovery for Resource-Constrained LLM Inference on Consumer Hardware The paper presents SparPR, a runtime key-value cache recovery mechanism that reduces memory bottlenecks during model inference on consumer hardware. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Does not study multi-step agents, planning, or tool use.
Attacking Only Half the Data: Two-Sided Evaluation of AI-Text Detector Robustness Evaluates AI text detectors under attacks applied to both human and machine text, revealing high false-positive rates on edited human writing. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation systems.
Attacking Only Half the Data: Two-Sided Evaluation of AI-Text Detector Robustness Evaluates AI text detectors under attacks applied to both human and machine text, revealing high false-positive rates on edited human writing. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Does not cover language model agents or planning.
Attacking Only Half the Data: Two-Sided Evaluation of AI-Text Detector Robustness Evaluates AI text detectors under attacks applied to both human and machine text, revealing high false-positive rates on edited human writing. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not cover model compression or inference acceleration.
Attacking Only Half the Data: Two-Sided Evaluation of AI-Text Detector Robustness Evaluates AI text detectors under attacks applied to both human and machine text, revealing high false-positive rates on edited human writing. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Covers text detection, not multimodal models.
Attacking Only Half the Data: Two-Sided Evaluation of AI-Text Detector Robustness Evaluates AI text detectors under attacks applied to both human and machine text, revealing high false-positive rates on edited human writing. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to code generation or software engineering.
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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on hallucination mitigation rather than model compression.
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	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not focus on coding or software engineering.
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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not use external retrieval or external knowledge bases.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on mitigation methods rather than evaluation benchmarks.
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	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Addresses diffusion language models, not multimodal data.

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