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
Can Language Models Learn When to Abstain? Policy Learning and Calibrated Answer Selection The paper studies policy learning and answer-selection mechanisms to help language models decide when to abstain on factual questions. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Evaluates factual QA datasets (TriviaQA, NQ), not software engineering or code generation.
Can Language Models Learn When to Abstain? Policy Learning and Calibrated Answer Selection The paper studies policy learning and answer-selection mechanisms to help language models decide when to abstain on factual questions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on calibrated abstention for QA rather than retrieval, chunking, or RAG architecture.
Can Language Models Learn When to Abstain? Policy Learning and Calibrated Answer Selection The paper studies policy learning and answer-selection mechanisms to help language models decide when to abstain on factual questions. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Work is strictly text-based question answering without vision, audio, or multimodal components.
OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation The paper presents an automatic pronunciation assessment method using Whisper cross-attention to predict word- and utterance-level scores without forced alignment. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an automated pronunciation scoring system rather than LLM benchmarking.
OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation The paper presents an automatic pronunciation assessment method using Whisper cross-attention to predict word- and utterance-level scores without forced alignment. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Deals with speech scoring, not retrieval-augmented generation.
OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation The paper presents an automatic pronunciation assessment method using Whisper cross-attention to predict word- and utterance-level scores without forced alignment. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses speech pronunciation evaluation, not AI deployment safety or guardrails.
OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation The paper presents an automatic pronunciation assessment method using Whisper cross-attention to predict word- and utterance-level scores without forced alignment. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on speech/audio assessment rather than multimodal text-image-video models.
OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation The paper presents an automatic pronunciation assessment method using Whisper cross-attention to predict word- and utterance-level scores without forced alignment. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on speech assessment accuracy rather than model compression or serving efficiency.
OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation The paper presents an automatic pronunciation assessment method using Whisper cross-attention to predict word- and utterance-level scores without forced alignment. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on speech pronunciation scoring, not autonomous AI agents.
OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation The paper presents an automatic pronunciation assessment method using Whisper cross-attention to predict word- and utterance-level scores without forced alignment. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Pertains to speech pronunciation assessment, not code models.
Structural Automata for Neural Semantic Parsing The paper proposes accepting-path decoding using structural automata to enforce valid prefix completions within length limits for neural semantic parsing. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses strictly on neural semantic parsing for text, not multimodal models.
Structural Automata for Neural Semantic Parsing The paper proposes accepting-path decoding using structural automata to enforce valid prefix completions within length limits for neural semantic parsing. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses formal syntax constraints during generation rather than deployment safety risks.
Structural Automata for Neural Semantic Parsing The paper proposes accepting-path decoding using structural automata to enforce valid prefix completions within length limits for neural semantic parsing. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on semantic parsing generation, not document retrieval or grounding.
Structural Automata for Neural Semantic Parsing The paper proposes accepting-path decoding using structural automata to enforce valid prefix completions within length limits for neural semantic parsing. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a decoding mechanism rather than an LLM evaluation benchmark.
Structural Automata for Neural Semantic Parsing The paper proposes accepting-path decoding using structural automata to enforce valid prefix completions within length limits for neural semantic parsing. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Covers semantic parsing decoding algorithms, not tool-using AI agents.
Structural Automata for Neural Semantic Parsing The paper proposes accepting-path decoding using structural automata to enforce valid prefix completions within length limits for neural semantic parsing. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on formal grammar decoding constraints rather than model efficiency or distillation.
Structural Automata for Neural Semantic Parsing The paper proposes accepting-path decoding using structural automata to enforce valid prefix completions within length limits for neural semantic parsing. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Investigates abstract meaning representation parsing rather than programming language code generation.
When VLMs Destroy What They Know: Spatial Representations Fall Below Lexical Priors The paper evaluates vision-language models' spatial representations using causally controlled perturbations, showing spatial relations degrade compared to lexical features. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Analyzes multimodal representation geometry rather than deployment safety guardrails.
When VLMs Destroy What They Know: Spatial Representations Fall Below Lexical Priors The paper evaluates vision-language models' spatial representations using causally controlled perturbations, showing spatial relations degrade compared to lexical features. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies internal representations of VLMs, not agent planning or tool execution.
When VLMs Destroy What They Know: Spatial Representations Fall Below Lexical Priors The paper evaluates vision-language models' spatial representations using causally controlled perturbations, showing spatial relations degrade compared to lexical features. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies spatial representations in vision-language models, not code LLMs.
When VLMs Destroy What They Know: Spatial Representations Fall Below Lexical Priors The paper evaluates vision-language models' spatial representations using causally controlled perturbations, showing spatial relations degrade compared to lexical features. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Analyzes representation loss rather than model compression or inference speed.
When VLMs Destroy What They Know: Spatial Representations Fall Below Lexical Priors The paper evaluates vision-language models' spatial representations using causally controlled perturbations, showing spatial relations degrade compared to lexical features. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on VLM internal representations, not retrieval-augmented generation.
RSI-Anything: Unlocking Recursive Self-Improvement with World Models The paper introduces a domain-general framework using predictive world models to guide action selection and policy improvement in recursive self-improvement settings. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on recursive self-improvement and world models, not retrieval-augmented generation.
RSI-Anything: Unlocking Recursive Self-Improvement with World Models The paper introduces a domain-general framework using predictive world models to guide action selection and policy improvement in recursive self-improvement settings. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Code repair is only one evaluation domain among many for a general self-improvement method.
RSI-Anything: Unlocking Recursive Self-Improvement with World Models The paper introduces a domain-general framework using predictive world models to guide action selection and policy improvement in recursive self-improvement settings. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on policy improvement loops, not model compression or inference efficiency.
RSI-Anything: Unlocking Recursive Self-Improvement with World Models The paper introduces a domain-general framework using predictive world models to guide action selection and policy improvement in recursive self-improvement settings. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a self-improvement method rather than an evaluation framework.
RSI-Anything: Unlocking Recursive Self-Improvement with World Models The paper introduces a domain-general framework using predictive world models to guide action selection and policy improvement in recursive self-improvement settings. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on performance improvement rather than deployment safety or guardrails.
RSI-Anything: Unlocking Recursive Self-Improvement with World Models The paper introduces a domain-general framework using predictive world models to guide action selection and policy improvement in recursive self-improvement settings. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Applies world models across domains rather than focusing on multimodal model capabilities.
Epistemic Disturbance in the Graph Model for Conflict Resolution: State-Preserving Actions, Four-Valued Assessments, and the Distinction between Capability and Intention The paper extends the Graph Model for Conflict Resolution by incorporating epistemic states and state-preserving actions into its decision-making framework. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Addresses formal conflict graph modeling, not LLM evaluation benchmarks.
Epistemic Disturbance in the Graph Model for Conflict Resolution: State-Preserving Actions, Four-Valued Assessments, and the Distinction between Capability and Intention The paper extends the Graph Model for Conflict Resolution by incorporating epistemic states and state-preserving actions into its decision-making framework. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on game-theoretic conflict models rather than operational AI deployment safety.
Epistemic Disturbance in the Graph Model for Conflict Resolution: State-Preserving Actions, Four-Valued Assessments, and the Distinction between Capability and Intention The paper extends the Graph Model for Conflict Resolution by incorporating epistemic states and state-preserving actions into its decision-making framework. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Formal game-theoretic conflict resolution paper, not LLM agents or tool use.
Can Jev be Your Q or Policy in Reinforcement Learning? Studies how the non-generative decision model Jev can serve as a prior or policy component within reinforcement learning frameworks. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Explores using a foundation model in RL rather than model compression or efficient serving.
Can Jev be Your Q or Policy in Reinforcement Learning? Studies how the non-generative decision model Jev can serve as a prior or policy component within reinforcement learning frameworks. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Investigates RL sample efficiency rather than safety or guardrails.
Can Jev be Your Q or Policy in Reinforcement Learning? Studies how the non-generative decision model Jev can serve as a prior or policy component within reinforcement learning frameworks. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Explores decision foundation models in RL pipelines, not multi-step language agents.
Can Jev be Your Q or Policy in Reinforcement Learning? Studies how the non-generative decision model Jev can serve as a prior or policy component within reinforcement learning frameworks. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents an RL algorithmic integration rather than an evaluation benchmark.
Elucidating the Space of Enzymatic Reaction: A Unified Benchmark and Pretrained Model Introduces a unified benchmark and pretrained encoder-decoder model for evaluating enzymatic reaction prediction and enzyme commission classifications. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Deals with computational biology rather than AI safety or reliability.
Elucidating the Space of Enzymatic Reaction: A Unified Benchmark and Pretrained Model Introduces a unified benchmark and pretrained encoder-decoder model for evaluating enzymatic reaction prediction and enzyme commission classifications. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on enzymatic reaction prediction models, not autonomous agents.
Elucidating the Space of Enzymatic Reaction: A Unified Benchmark and Pretrained Model Introduces a unified benchmark and pretrained encoder-decoder model for evaluating enzymatic reaction prediction and enzyme commission classifications. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a biochemical reaction benchmark rather than an LLM evaluation framework.
A 3D Characterization Framework for Intelligent Sequential Decision Making Proposes a three-dimensional framework evaluating decision-making artificial intelligence methods across sequential formalisms, autonomy levels, and computational cost. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on puzzle solving and decision formalisms, not safety or guardrails.
Intervention anchors and scientific verification in synthetic vascular predictive representations Presents a mathematical audit on how intervention anchors and coordinate transport affect stability in synthetic vascular predictive representations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Covers biomedical coordinate verification rather than AI system deployment safety.
Intervention anchors and scientific verification in synthetic vascular predictive representations Presents a mathematical audit on how intervention anchors and coordinate transport affect stability in synthetic vascular predictive representations. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Pertains to synthetic vascular representation audits, not AI agents.
Intervention anchors and scientific verification in synthetic vascular predictive representations Presents a mathematical audit on how intervention anchors and coordinate transport affect stability in synthetic vascular predictive representations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on synthetic biomedical representation math rather than LLM benchmarks.
DeltaReplay: Task-Relative Memory Reuse for Mobile GUI Agents Introduces a step-level memory reuse framework using transition graphs to adapt relevant prior mobile graphical interface execution trajectories. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an agent framework rather than an LLM evaluation benchmark.
DeltaReplay: Task-Relative Memory Reuse for Mobile GUI Agents Introduces a step-level memory reuse framework using transition graphs to adapt relevant prior mobile graphical interface execution trajectories. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on GUI task completion efficiency rather than safety or reliability.
Internalizer: Portable Context-to-Parameter Mapping for Very Large Language Models Presents a hypernetwork that maps document contexts directly into low-rank adapter weights for large language models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a parameter mapping technique rather than an evaluation benchmark.
Internalizer: Portable Context-to-Parameter Mapping for Very Large Language Models Presents a hypernetwork that maps document contexts directly into low-rank adapter weights for large language models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Proposes a hypernetwork for model adapters, not autonomous agents.
Internalizer: Portable Context-to-Parameter Mapping for Very Large Language Models Presents a hypernetwork that maps document contexts directly into low-rank adapter weights for large language models. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on context parameterization, not software engineering or code generation.
Internalizer: Portable Context-to-Parameter Mapping for Very Large Language Models Presents a hypernetwork that maps document contexts directly into low-rank adapter weights for large language models. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals with text documents and language models, not multimodal models.
Internalizer: Portable Context-to-Parameter Mapping for Very Large Language Models Presents a hypernetwork that maps document contexts directly into low-rank adapter weights for large language models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Proposes an architectural efficiency technique, not safety or guardrails.
MultiWorldBench: Do Independently Controlled Views Describe One Shared World? Introduces a Minecraft diagnostic benchmark testing whether multiplayer world models maintain consistent and persistent representations across independently controlled views. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Examines multi-view world models rather than planning agents.
MultiWorldBench: Do Independently Controlled Views Describe One Shared World? Introduces a Minecraft diagnostic benchmark testing whether multiplayer world models maintain consistent and persistent representations across independently controlled views. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on synthetic Minecraft world simulation, not industrial computer vision.
MultiWorldBench: Do Independently Controlled Views Describe One Shared World? Introduces a Minecraft diagnostic benchmark testing whether multiplayer world models maintain consistent and persistent representations across independently controlled views. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Evaluates simulated world models rather than deployment safety or jailbreak risks.
MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning Presents a multi-dimensional data pruning framework that filters code repositories by syntax, diversity, and representation for efficient retriever fine-tuning. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a data pruning method rather than an evaluation benchmark.
MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning Presents a multi-dimensional data pruning framework that filters code repositories by syntax, diversity, and representation for efficient retriever fine-tuning. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on code retrieval training data pruning, not multi-step agents.
MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning Presents a multi-dimensional data pruning framework that filters code repositories by syntax, diversity, and representation for efficient retriever fine-tuning. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses data pruning efficiency, not AI safety or red-teaming.
MemTrial: Learning When to Trust Memory in LLM Portfolio Agents Presents a portfolio management agent that uses fractional factorial counterfactual evaluation and Banzhaf values to isolate memory utility from market movements. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on financial decision policy optimization rather than AI safety guardrails.
MemTrial: Learning When to Trust Memory in LLM Portfolio Agents Presents a portfolio management agent that uses fractional factorial counterfactual evaluation and Banzhaf values to isolate memory utility from market movements. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Designs an agent decision mechanism rather than an LLM evaluation benchmark.
Timer-M1: A Multivariate Time Series Foundation Model via Learning Primitives Introduces a multivariate time series foundation model pretrained using synthetic temporal and relational pattern primitives for zero-shot forecasting. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on pretraining time-series architectures, not model compression or efficiency.
Timer-M1: A Multivariate Time Series Foundation Model via Learning Primitives Introduces a multivariate time series foundation model pretrained using synthetic temporal and relational pattern primitives for zero-shot forecasting. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents a time series foundation model, not an agent system.
Timer-M1: A Multivariate Time Series Foundation Model via Learning Primitives Introduces a multivariate time series foundation model pretrained using synthetic temporal and relational pattern primitives for zero-shot forecasting. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Pertains to time-series forecasting accuracy, not AI safety or alignment.

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