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
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 safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address LLM safety, guardrails, or alignment.
LLM Agents for Automated Post-Training via Structured Recipe Optimization The paper introduces an agent-based approach that adapts search spaces to optimize post-training recipes and configurations for pre-trained language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on post-training adaptation recipes rather than efficient model inference.
LLM Agents for Automated Post-Training via Structured Recipe Optimization The paper introduces an agent-based approach that adapts search spaces to optimize post-training recipes and configurations for pre-trained language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on automated training recipe search rather than benchmark evaluation.
LLM Agents for Automated Post-Training via Structured Recipe Optimization The paper introduces an agent-based approach that adapts search spaces to optimize post-training recipes and configurations for pre-trained language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on post-training optimization rather than safety or guardrails.
The Many Morals of AI: The cautionary bias of North American news narratives in global perspective The paper analyzes implicit morals in news coverage of artificial intelligence across four global regions, identifying distinct regional variations in narrative caution. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies media narratives about AI, not LLM agent systems.
The Many Morals of AI: The cautionary bias of North American news narratives in global perspective The paper analyzes implicit morals in news coverage of artificial intelligence across four global regions, identifying distinct regional variations in narrative caution. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not investigate retrieval-augmented generation methods.
The Many Morals of AI: The cautionary bias of North American news narratives in global perspective The paper analyzes implicit morals in news coverage of artificial intelligence across four global regions, identifying distinct regional variations in narrative caution. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to code generation or software engineering LLMs.
The Many Morals of AI: The cautionary bias of North American news narratives in global perspective The paper analyzes implicit morals in news coverage of artificial intelligence across four global regions, identifying distinct regional variations in narrative caution. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes news discourse rather than evaluating LLM capabilities or benchmarks.
The Many Morals of AI: The cautionary bias of North American news narratives in global perspective The paper analyzes implicit morals in news coverage of artificial intelligence across four global regions, identifying distinct regional variations in narrative caution. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on multimodal model architectures or capabilities.
The Many Morals of AI: The cautionary bias of North American news narratives in global perspective The paper analyzes implicit morals in news coverage of artificial intelligence across four global regions, identifying distinct regional variations in narrative caution. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study small or efficient model architectures.
The Many Morals of AI: The cautionary bias of North American news narratives in global perspective The paper analyzes implicit morals in news coverage of artificial intelligence across four global regions, identifying distinct regional variations in narrative caution. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Sociological study of news framing rather than technical AI safety.
When More Objectives Make Identification Easier: The Role of Preferences and Feedback The paper theoretically analyzes multi-objective best-arm identification to determine when multiple criteria reduce exploration requirements in multi-armed bandit settings. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies theoretical multi-objective bandits, not language model evaluation.
When More Objectives Make Identification Easier: The Role of Preferences and Feedback The paper theoretically analyzes multi-objective best-arm identification to determine when multiple criteria reduce exploration requirements in multi-armed bandit settings. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or efficient serving.
When More Objectives Make Identification Easier: The Role of Preferences and Feedback The paper theoretically analyzes multi-objective best-arm identification to determine when multiple criteria reduce exploration requirements in multi-armed bandit settings. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on theoretical bandit optimization, not AI safety risks.
When More Objectives Make Identification Easier: The Role of Preferences and Feedback The paper theoretically analyzes multi-objective best-arm identification to determine when multiple criteria reduce exploration requirements in multi-armed bandit settings. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical bandit theory paper, not focused on LLM agents.
Compare to Correct: Pairwise-Guided Logit Adjustment for LLM Test-Time Scaling The paper introduces a logit adjustment method based on pairwise comparisons between sampled reasoning paths to improve test-time answer selection in language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on test-time sampling aggregation, not multi-step agent actions.
Compare to Correct: Pairwise-Guided Logit Adjustment for LLM Test-Time Scaling The paper introduces a logit adjustment method based on pairwise comparisons between sampled reasoning paths to improve test-time answer selection in language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a decoding adjustment method rather than an evaluation framework.
GeoMER: A Joint Euclidean-Hyperbolic Geometry Framework for Multimodal Emotion Recognition The paper presents a multimodal emotion recognition framework that integrates Euclidean and hyperbolic geometry to capture hierarchical emotion structures from text and speech. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Not concerned with AI safety, reliability, or deployment risks.
GeoMER: A Joint Euclidean-Hyperbolic Geometry Framework for Multimodal Emotion Recognition The paper presents a multimodal emotion recognition framework that integrates Euclidean and hyperbolic geometry to capture hierarchical emotion structures from text and speech. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies multimodal emotion recognition, not LLM agents or tool use.
GeoMER: A Joint Euclidean-Hyperbolic Geometry Framework for Multimodal Emotion Recognition The paper presents a multimodal emotion recognition framework that integrates Euclidean and hyperbolic geometry to capture hierarchical emotion structures from text and speech. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a representation learning method, not an LLM evaluation.
GeoMER: A Joint Euclidean-Hyperbolic Geometry Framework for Multimodal Emotion Recognition The paper presents a multimodal emotion recognition framework that integrates Euclidean and hyperbolic geometry to capture hierarchical emotion structures from text and speech. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, quantization, or efficient inference.
ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models The paper proposes a continual off-policy reinforcement learning framework that reuses actor and critic networks across sequential robotic manipulation tasks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on robot reinforcement learning, not LLM agent workflows.
ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models The paper proposes a continual off-policy reinforcement learning framework that reuses actor and critic networks across sequential robotic manipulation tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotic reinforcement learning rather than industrial visual inspection.
ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models The paper proposes a continual off-policy reinforcement learning framework that reuses actor and critic networks across sequential robotic manipulation tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a robotic RL algorithm, not an LLM benchmark.
ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models The paper proposes a continual off-policy reinforcement learning framework that reuses actor and critic networks across sequential robotic manipulation tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address AI safety, jailbreaks, or deployment guardrails.
ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models The paper proposes a continual off-policy reinforcement learning framework that reuses actor and critic networks across sequential robotic manipulation tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Investigates continual RL sample efficiency, not model compression or serving.
PhysicsSliders: Learning Physical Controls for Audio Foley Generation The paper presents a generative audio method that controls Foley sound synthesis using physical interaction, material, and geometry parameters. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model efficiency, compression, or fast inference.
PhysicsSliders: Learning Physical Controls for Audio Foley Generation The paper presents a generative audio method that controls Foley sound synthesis using physical interaction, material, and geometry parameters. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on audio foley synthesis, not LLM agent systems.
PhysicsSliders: Learning Physical Controls for Audio Foley Generation The paper presents a generative audio method that controls Foley sound synthesis using physical interaction, material, and geometry parameters. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates audio generation physics, not general LLM benchmarking.
PhysicsSliders: Learning Physical Controls for Audio Foley Generation The paper presents a generative audio method that controls Foley sound synthesis using physical interaction, material, and geometry parameters. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Not related to AI safety, hallucinations, or deployment guardrails.
Rethinking Projector Training in Multimodal LLMs The paper examines projector training in multimodal models and proposes a training-free initialization method using summary statistics instead of paired image-caption data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an alignment method rather than evaluation benchmarks or methodology.
Rethinking Projector Training in Multimodal LLMs The paper examines projector training in multimodal models and proposes a training-free initialization method using summary statistics instead of paired image-caption data. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Studies general vision-language alignment rather than manufacturing defect inspection.
Rethinking Projector Training in Multimodal LLMs The paper examines projector training in multimodal models and proposes a training-free initialization method using summary statistics instead of paired image-caption data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Analyzes multimodal representation geometry rather than safety or security risks.
Rethinking Projector Training in Multimodal LLMs The paper examines projector training in multimodal models and proposes a training-free initialization method using summary statistics instead of paired image-caption data. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates multimodal projector training, not multi-step agent planning or tools.
Rethinking Projector Training in Multimodal LLMs The paper examines projector training in multimodal models and proposes a training-free initialization method using summary statistics instead of paired image-caption data. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on vision-language feature projection rather than external document retrieval.
Rethinking Projector Training in Multimodal LLMs The paper examines projector training in multimodal models and proposes a training-free initialization method using summary statistics instead of paired image-caption data. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Deals with vision-language projector training rather than software engineering code.
Auditing Future-Memory Use in Point-in-Time Language Agents The paper audits historical evaluation tasks to see whether an agent's internal post-cutoff memory skews its point-in-time evidence gathering. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Audits a specific model behavior rather than introducing evaluation benchmarks.
LLM2R: From Large Language Models to 3D Reassemblers The paper adapts a causal language model using compressed 3D geometric tokens to assemble multi-part objects in a single forward pass. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a model architecture for 3D assembly, not evaluation benchmarks.
LLM2R: From Large Language Models to 3D Reassemblers The paper adapts a causal language model using compressed 3D geometric tokens to assemble multi-part objects in a single forward pass. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on 3D geometric shape reassembly rather than industrial defect detection.
LLM2R: From Large Language Models to 3D Reassemblers The paper adapts a causal language model using compressed 3D geometric tokens to assemble multi-part objects in a single forward pass. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on 3D geometric assembly rather than safety or reliability.
LLM2R: From Large Language Models to 3D Reassemblers The paper adapts a causal language model using compressed 3D geometric tokens to assemble multi-part objects in a single forward pass. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Applies transformer architectures to 3D shape assembly, not autonomous agent workflows.
Beyond Answer Voting: Fine-Grained Correction of LLM Reasoning through Graph Alignment across Sampled Traces The paper proposes a framework that uses reasoning graphs to locate claim-level disagreements across sampled traces and correct multi-step language model reasoning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on graph-aligned reasoning correction rather than model compression.
Beyond Answer Voting: Fine-Grained Correction of LLM Reasoning through Graph Alignment across Sampled Traces The paper proposes a framework that uses reasoning graphs to locate claim-level disagreements across sampled traces and correct multi-step language model reasoning. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Addresses text-based reasoning without any image or multimodal components.
Beyond Answer Voting: Fine-Grained Correction of LLM Reasoning through Graph Alignment across Sampled Traces The paper proposes a framework that uses reasoning graphs to locate claim-level disagreements across sampled traces and correct multi-step language model reasoning. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Deals with general reasoning verification rather than software engineering or code.
Beyond Answer Voting: Fine-Grained Correction of LLM Reasoning through Graph Alignment across Sampled Traces The paper proposes a framework that uses reasoning graphs to locate claim-level disagreements across sampled traces and correct multi-step language model reasoning. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on multi-trace answer verification rather than interactive agent workflows.
Beyond Answer Voting: Fine-Grained Correction of LLM Reasoning through Graph Alignment across Sampled Traces The paper proposes a framework that uses reasoning graphs to locate claim-level disagreements across sampled traces and correct multi-step language model reasoning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an inference-time verification method rather than benchmark evaluation.
Beyond Answer Voting: Fine-Grained Correction of LLM Reasoning through Graph Alignment across Sampled Traces The paper proposes a framework that uses reasoning graphs to locate claim-level disagreements across sampled traces and correct multi-step language model reasoning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Aligns reasoning graphs across sampled traces rather than retrieving external documents.
DPOison-VL: Clean Preference Poisoning in Vision-Language Direct Preference Optimization The paper introduces an imperceptible image perturbation attack that manipulates alignment in vision-language models during preference optimization without altering text annotations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies preference poisoning attacks rather than autonomous agent systems.
DPOison-VL: Clean Preference Poisoning in Vision-Language Direct Preference Optimization The paper introduces an imperceptible image perturbation attack that manipulates alignment in vision-language models during preference optimization without altering text annotations. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on vision-language model security rather than industrial defect inspection.
DPOison-VL: Clean Preference Poisoning in Vision-Language Direct Preference Optimization The paper introduces an imperceptible image perturbation attack that manipulates alignment in vision-language models during preference optimization without altering text annotations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Develops an attack method rather than evaluation frameworks or general benchmarks.
Jev-ToM: Evaluating Theory of Mind in Typed Decision Models The paper introduces a benchmark to evaluate how typed decision models handle theory-of-mind reasoning tasks compared to standard generative language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Studies theory-of-mind question selection rather than retrieval-augmented generation.
Jev-ToM: Evaluating Theory of Mind in Typed Decision Models The paper introduces a benchmark to evaluate how typed decision models handle theory-of-mind reasoning tasks compared to standard generative language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on English text Theory of Mind benchmarks without multimodal data.
Jev-ToM: Evaluating Theory of Mind in Typed Decision Models The paper introduces a benchmark to evaluate how typed decision models handle theory-of-mind reasoning tasks compared to standard generative language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Tests theory of mind selection rather than multi-step tool-using agents.
Jev-ToM: Evaluating Theory of Mind in Typed Decision Models The paper introduces a benchmark to evaluate how typed decision models handle theory-of-mind reasoning tasks compared to standard generative language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies cognitive modeling and selection accuracy rather than safety guardrails.
Jev-ToM: Evaluating Theory of Mind in Typed Decision Models The paper introduces a benchmark to evaluate how typed decision models handle theory-of-mind reasoning tasks compared to standard generative language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Examines typed cognitive models rather than code generation or software repair.
Nudge the Judge: Uncovering Program Decision Hijacking in Real-World Jev Applications The paper presents an automated framework to evaluate how adversarial contexts can redirect program execution through valid structured judgments in language-model applications. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a vulnerability testing framework for specific apps rather than LLM benchmarks.
Nudge the Judge: Uncovering Program Decision Hijacking in Real-World Jev Applications The paper presents an automated framework to evaluate how adversarial contexts can redirect program execution through valid structured judgments in language-model applications. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies program decision hijacking in decision models rather than autonomous agents.
Does self-distillation improve reasoning? The paper evaluates whether self-distillation using privileged information improves reasoning capabilities or reflects overfitting to training tasks. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-based reasoning rather than multimodal image or video models.
Does self-distillation improve reasoning? The paper evaluates whether self-distillation using privileged information improves reasoning capabilities or reflects overfitting to training tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Analyzes training mechanics rather than safety, guardrails, or alignment risks.
Does self-distillation improve reasoning? The paper evaluates whether self-distillation using privileged information improves reasoning capabilities or reflects overfitting to training tasks. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Evaluates reasoning methods across domains rather than focusing on software engineering.

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