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
AssetForge: Asset-Augmented Task Synthesis for Business Automation Agents AssetForge pairs reusable application assets with task rubrics to synthesize executable training tasks and evaluation checks for business automation agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focus is task synthesis and training, not safety or guardrails.
AssetForge: Asset-Augmented Task Synthesis for Business Automation Agents AssetForge pairs reusable application assets with task rubrics to synthesize executable training tasks and evaluation checks for business automation agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focus is synthetic data generation for agent training, not evaluation.
Lightweight but Linguistically Fair? Quantisation Robustness across Bavarian, German, and English The authors evaluate how 8-bit and 4-bit quantization methods affect model accuracy across Bavarian, Standard German, and English benchmarks. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates text-only language models, not multimodal systems.
Lightweight but Linguistically Fair? Quantisation Robustness across Bavarian, German, and English The authors evaluate how 8-bit and 4-bit quantization methods affect model accuracy across Bavarian, Standard German, and English benchmarks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Analyzes quantization fairness, not deployment safety or guardrails.
Lightweight but Linguistically Fair? Quantisation Robustness across Bavarian, German, and English The authors evaluate how 8-bit and 4-bit quantization methods affect model accuracy across Bavarian, Standard German, and English benchmarks. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focus is natural language dialects, not code generation.
Lightweight but Linguistically Fair? Quantisation Robustness across Bavarian, German, and English The authors evaluate how 8-bit and 4-bit quantization methods affect model accuracy across Bavarian, Standard German, and English benchmarks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not study external document retrieval or grounding.
Lightweight but Linguistically Fair? Quantisation Robustness across Bavarian, German, and English The authors evaluate how 8-bit and 4-bit quantization methods affect model accuracy across Bavarian, Standard German, and English benchmarks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Uses an existing benchmark to study quantization fairness.
Lightweight but Linguistically Fair? Quantisation Robustness across Bavarian, German, and English The authors evaluate how 8-bit and 4-bit quantization methods affect model accuracy across Bavarian, Standard German, and English benchmarks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Paper is about model quantization, not agent planning.
Let the Mind Conceive, Let the Hand Achieve: Following Multimodal Instructions for Long-Horizon Robotic Manipulation Orchestra decomposes multimodal instructions into subtasks, tracks step progress, and maintains execution context to improve long-horizon robotic manipulation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a robotics benchmark merely to test its specific method.
Let the Mind Conceive, Let the Hand Achieve: Following Multimodal Instructions for Long-Horizon Robotic Manipulation Orchestra decomposes multimodal instructions into subtasks, tracks step progress, and maintains execution context to improve long-horizon robotic manipulation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on general robotic manipulation planning, not industrial inspection.
Let the Mind Conceive, Let the Hand Achieve: Following Multimodal Instructions for Long-Horizon Robotic Manipulation Orchestra decomposes multimodal instructions into subtasks, tracks step progress, and maintains execution context to improve long-horizon robotic manipulation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not focus on safety guardrails or deployment reliability risks.
Choosing LLMs for Strategic Agent Interactions The authors formulate a meta-game framework to dynamically route and select language models during strategic multi-agent interactions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not investigate retrieval-augmented generation systems.
Choosing LLMs for Strategic Agent Interactions The authors formulate a meta-game framework to dynamically route and select language models during strategic multi-agent interactions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focus is strategic decision-making benchmarks, not AI safety guardrails.
ABot-ER: Unified Embodied Reasoning through Agentic Data-Model Co-Evolution The authors introduce an embodied reasoning foundation model that unifies perception, planning, and execution using task-conditioned adaptive adapters. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focus is an embodied agent architecture, not evaluating benchmarks.
ABot-ER: Unified Embodied Reasoning through Agentic Data-Model Co-Evolution The authors introduce an embodied reasoning foundation model that unifies perception, planning, and execution using task-conditioned adaptive adapters. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address safety, guardrails, or deployment reliability risks.
Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes The authors present an active spatial search framework enabling vision-language agents to ground 3D objects in city-scale environments via interactive tool use. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Active 3D grounding framework, not safety or guardrails.
Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes The authors present an active spatial search framework enabling vision-language agents to ground 3D objects in city-scale environments via interactive tool use. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on outdoor city-scale grounding, not industrial manufacturing inspection.
Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes The authors present an active spatial search framework enabling vision-language agents to ground 3D objects in city-scale environments via interactive tool use. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a grounding framework, not a general evaluation benchmark.
Delegating Earth science model calibration to knowledge-guided LLM agents The authors introduce a calibration harness using structured domain knowledge to safely delegate Earth science model parameterization to language model agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an agent workflow harness, not an LLM evaluation benchmark.
PRISM: Calibrating Black-Box Judges with Counterfactual Probes The authors propose a probe-based method to measure and calibrate systematic scoring biases in black-box LLM evaluation judges. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model efficiency, pruning, or quantization.
PRISM: Calibrating Black-Box Judges with Counterfactual Probes The authors propose a probe-based method to measure and calibrate systematic scoring biases in black-box LLM evaluation judges. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates text summarization and generation, not multimodal models.
PRISM: Calibrating Black-Box Judges with Counterfactual Probes The authors propose a probe-based method to measure and calibrate systematic scoring biases in black-box LLM evaluation judges. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on code LLMs or software engineering.
PRISM: Calibrating Black-Box Judges with Counterfactual Probes The authors propose a probe-based method to measure and calibrate systematic scoring biases in black-box LLM evaluation judges. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation or grounding.
PRISM: Calibrating Black-Box Judges with Counterfactual Probes The authors propose a probe-based method to measure and calibrate systematic scoring biases in black-box LLM evaluation judges. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on calibrating evaluation scores, not LLM agents.
Graph-free Retrieval with Graph-Modified Embeddings The authors evaluate offline graph-modified embeddings for vector retrieval, improving multi-hop evidence recovery without requiring query-time graph access. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a retrieval method, not an LLM benchmark or evaluation protocol.
Graph-free Retrieval with Graph-Modified Embeddings The authors evaluate offline graph-modified embeddings for vector retrieval, improving multi-hop evidence recovery without requiring query-time graph access. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Information retrieval paper, not AI safety or guardrails.
Graph-free Retrieval with Graph-Modified Embeddings The authors evaluate offline graph-modified embeddings for vector retrieval, improving multi-hop evidence recovery without requiring query-time graph access. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on vector retrieval techniques, not autonomous multi-step agents.
Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs The authors trace internal representations across prompt optimization techniques to show how discrete prompts, prompt tuning, and prefixes alter model processing. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Prompt mechanics study, not deployment safety or alignment.
Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs The authors trace internal representations across prompt optimization techniques to show how discrete prompts, prompt tuning, and prefixes alter model processing. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on code generation or software tasks.
Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs The authors trace internal representations across prompt optimization techniques to show how discrete prompts, prompt tuning, and prefixes alter model processing. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Mechanistic analysis of prompts rather than model compression or serving efficiency.
Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs The authors trace internal representations across prompt optimization techniques to show how discrete prompts, prompt tuning, and prefixes alter model processing. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Examines text language models, not multimodal models.
Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs The authors trace internal representations across prompt optimization techniques to show how discrete prompts, prompt tuning, and prefixes alter model processing. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not examine retrieval-augmented generation systems.
Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs The authors trace internal representations across prompt optimization techniques to show how discrete prompts, prompt tuning, and prefixes alter model processing. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies internal prompt adaptation mechanics, not autonomous agents.
Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison The authors reproduce and compare five language model factuality alignment methods on a standardized benchmark across identical base models and evaluation protocols. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text factuality, not multimodal models.
Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison The authors reproduce and compare five language model factuality alignment methods on a standardized benchmark across identical base models and evaluation protocols. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on software engineering or code generation.
Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison The authors reproduce and compare five language model factuality alignment methods on a standardized benchmark across identical base models and evaluation protocols. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies factuality alignment algorithms, not agent planning or tool use.
Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison The authors reproduce and compare five language model factuality alignment methods on a standardized benchmark across identical base models and evaluation protocols. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, pruning, or efficient inference.
Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison The authors reproduce and compare five language model factuality alignment methods on a standardized benchmark across identical base models and evaluation protocols. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Investigates parametric factuality alignment, not retrieval-augmented generation.
Resource-Efficient 3D Shape and Orientation Estimation of Road Users using Roadside RGB Camera and 3D Radar The authors propose a resource-efficient two-stage approach for estimating 3D road user shapes and orientations from roadside cameras and radar. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on intelligent transportation systems, not industrial inspection or defect detection.
Resource-Efficient 3D Shape and Orientation Estimation of Road Users using Roadside RGB Camera and 3D Radar The authors propose a resource-efficient two-stage approach for estimating 3D road user shapes and orientations from roadside cameras and radar. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Roadside traffic perception system, not an LLM agent.
Resource-Efficient 3D Shape and Orientation Estimation of Road Users using Roadside RGB Camera and 3D Radar The authors propose a resource-efficient two-stage approach for estimating 3D road user shapes and orientations from roadside cameras and radar. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Computer vision perception model, not an LLM benchmark.
Resource-Efficient 3D Shape and Orientation Estimation of Road Users using Roadside RGB Camera and 3D Radar The authors propose a resource-efficient two-stage approach for estimating 3D road user shapes and orientations from roadside cameras and radar. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Autonomous driving perception, not AI deployment safety or alignment.
Agaram 2.5B: The First Tamil-First Language Model Trained from Scratch with Tool Calling. A Playbook for Under-Represented Languages The authors introduce a 2.5-billion-parameter bilingual Tamil-English language model trained from scratch with tool-calling capabilities. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on code generation or software engineering tasks.
Agaram 2.5B: The First Tamil-First Language Model Trained from Scratch with Tool Calling. A Playbook for Under-Represented Languages The authors introduce a 2.5-billion-parameter bilingual Tamil-English language model trained from scratch with tool-calling capabilities. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Training playbook for low-resource languages, not an evaluation methodology.
Agaram 2.5B: The First Tamil-First Language Model Trained from Scratch with Tool Calling. A Playbook for Under-Represented Languages The authors introduce a 2.5-billion-parameter bilingual Tamil-English language model trained from scratch with tool-calling capabilities. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Text-only language model, not multimodal.
Agaram 2.5B: The First Tamil-First Language Model Trained from Scratch with Tool Calling. A Playbook for Under-Represented Languages The authors introduce a 2.5-billion-parameter bilingual Tamil-English language model trained from scratch with tool-calling capabilities. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Not focused on retrieval-augmented generation or external document retrieval.
Agaram 2.5B: The First Tamil-First Language Model Trained from Scratch with Tool Calling. A Playbook for Under-Represented Languages The authors introduce a 2.5-billion-parameter bilingual Tamil-English language model trained from scratch with tool-calling capabilities. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Not focused on AI safety, red-teaming, or guardrails.
Agaram 2.5B: The First Tamil-First Language Model Trained from Scratch with Tool Calling. A Playbook for Under-Represented Languages The authors introduce a 2.5-billion-parameter bilingual Tamil-English language model trained from scratch with tool-calling capabilities. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Mentions basic tool calling in passing; main focus is language model training.
FedGraph: Defending Federated Large Language Model Fine-Tuning Against Backdoor Attacks via Graph-Based Aggregation The authors present a graph-based aggregation method to defend federated language model fine-tuning against backdoor attacks by identifying update consistency patterns. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a defense framework, not an LLM benchmark or evaluation study.
FedGraph: Defending Federated Large Language Model Fine-Tuning Against Backdoor Attacks via Graph-Based Aggregation The authors present a graph-based aggregation method to defend federated language model fine-tuning against backdoor attacks by identifying update consistency patterns. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on federated learning defenses, not agent planning or execution.
How Well Do LLMs Perform in Peer Review? Evidence from Finance and Accounting The authors evaluate language models on finance peer review tasks, finding predictive ability for publication outcomes through correctness rather than originality assessments. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies LLMs evaluating papers, not multi-step agent planning or tool use.
STAMP as a topology-aware molecular language for fragment-level learning and editing The authors introduce a topology-aware molecular representation language that enables fragment-level representation learning and precise structural edits. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Computational chemistry paper, not AI safety or deployment reliability.
STAMP as a topology-aware molecular language for fragment-level learning and editing The authors introduce a topology-aware molecular representation language that enables fragment-level representation learning and precise structural edits. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on molecular editing representations, not LLM evaluation.
STAMP as a topology-aware molecular language for fragment-level learning and editing The authors introduce a topology-aware molecular representation language that enables fragment-level representation learning and precise structural edits. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes molecular representation for chemistry, not LLM agent systems.
The Singularity Has a Distribution: Exact Timing Laws and Detection Limits for a Stochastic Intelligence Explosion The authors formulate a stochastic model to analyze the timing distributions and detection limits of potential recursive artificial intelligence explosions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Theoretical timing analysis rather than LLM benchmarking or evaluation.
The Singularity Has a Distribution: Exact Timing Laws and Detection Limits for a Stochastic Intelligence Explosion The authors formulate a stochastic model to analyze the timing distributions and detection limits of potential recursive artificial intelligence explosions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Theoretical singularity modeling rather than practical deployment safety or reliability.
The Singularity Has a Distribution: Exact Timing Laws and Detection Limits for a Stochastic Intelligence Explosion The authors formulate a stochastic model to analyze the timing distributions and detection limits of potential recursive artificial intelligence explosions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical mathematical modeling of recursive self-improvement, not AI agents.
Thinker-WM: A Unified World Model with Shared Multimodal Representations for Embodied Intelligence The authors propose an embodied world model that unifies RGB video, metric depth, and robot actions into a shared dynamic representation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on embodied robotics, not industrial manufacturing defect detection.
Thinker-WM: A Unified World Model with Shared Multimodal Representations for Embodied Intelligence The authors propose an embodied world model that unifies RGB video, metric depth, and robot actions into a shared dynamic representation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a world model architecture, not a benchmark or evaluation method.
Thinker-WM: A Unified World Model with Shared Multimodal Representations for Embodied Intelligence The authors propose an embodied world model that unifies RGB video, metric depth, and robot actions into a shared dynamic representation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Not focused on safety guardrails, jailbreaks, or deployment risks.

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