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
Rethinking Human Intent-to-CAD: Parametric CAD Model Generation via Cooperative Multi-Task Alignment and Spatial-Aware Reinforcement Learning The paper presents a dataset and framework mapping sketches and text to executable parametric computer-aided design code. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a CAD dataset/method, not an LLM benchmark.
Rethinking Human Intent-to-CAD: Parametric CAD Model Generation via Cooperative Multi-Task Alignment and Spatial-Aware Reinforcement Learning The paper presents a dataset and framework mapping sketches and text to executable parametric computer-aided design code. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on generative CAD modeling, not multi-step LLM agents.
Rethinking Human Intent-to-CAD: Parametric CAD Model Generation via Cooperative Multi-Task Alignment and Spatial-Aware Reinforcement Learning The paper presents a dataset and framework mapping sketches and text to executable parametric computer-aided design code. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Concerns CAD code generation, not AI deployment safety.
Rethinking Human Intent-to-CAD: Parametric CAD Model Generation via Cooperative Multi-Task Alignment and Spatial-Aware Reinforcement Learning The paper presents a dataset and framework mapping sketches and text to executable parametric computer-aided design code. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on CAD generation from sketches, not industrial defect detection.
Radioactive Feedback: Tracing Models Trained by Label-Only LLM Supervision The paper proposes an evaluation technique that embeds behavioral traces into score-only feedback to trace distilled student models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on distillation watermarking, not agent tool use or planning.
Radioactive Feedback: Tracing Models Trained by Label-Only LLM Supervision The paper proposes an evaluation technique that embeds behavioral traces into score-only feedback to trace distilled student models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies distillation provenance, not general evaluation methodologies.
FARL: A Fully Automated Pipeline of RL Interface and Solver Design The paper presents an automated framework that jointly designs reinforcement learning observation spaces, reward programs, algorithms, and training configurations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL pipeline optimization, not deployment safety.
FARL: A Fully Automated Pipeline of RL Interface and Solver Design The paper presents an automated framework that jointly designs reinforcement learning observation spaces, reward programs, algorithms, and training configurations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an automated RL search pipeline, not a benchmark.
TabMosaic: Unified In-Context Diffusion for Tabular Generative Tasks The paper presents an in-context diffusion model for generating, completing, and synthesizing unseen tabular data records jointly. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers tabular diffusion models, not LLM-based autonomous agents.
TabMosaic: Unified In-Context Diffusion for Tabular Generative Tasks The paper presents an in-context diffusion model for generating, completing, and synthesizing unseen tabular data records jointly. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses tabular data generation, not AI deployment safety or jailbreaks.
TabMosaic: Unified In-Context Diffusion for Tabular Generative Tasks The paper presents an in-context diffusion model for generating, completing, and synthesizing unseen tabular data records jointly. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a tabular generative model, not an evaluation benchmark.
TabMosaic: Unified In-Context Diffusion for Tabular Generative Tasks The paper presents an in-context diffusion model for generating, completing, and synthesizing unseen tabular data records jointly. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on in-context diffusion for tables, not model compression.
RespFocus: Learning Temporal Evidence for Weakly Supervised Respiratory Sound Classification The paper introduces a weakly supervised learning framework and dataset for identifying acoustic evidence in respiratory sound classification. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Medical sound dataset study, not general LLM evaluation.
RespFocus: Learning Temporal Evidence for Weakly Supervised Respiratory Sound Classification The paper introduces a weakly supervised learning framework and dataset for identifying acoustic evidence in respiratory sound classification. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with weakly supervised acoustic classification, not model efficiency.
RespFocus: Learning Temporal Evidence for Weakly Supervised Respiratory Sound Classification The paper introduces a weakly supervised learning framework and dataset for identifying acoustic evidence in respiratory sound classification. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on sound classification, not LLM planning or agents.
RespFocus: Learning Temporal Evidence for Weakly Supervised Respiratory Sound Classification The paper introduces a weakly supervised learning framework and dataset for identifying acoustic evidence in respiratory sound classification. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses medical diagnosis classification, not deployment safety or guardrails.
bharatSetu: a 1M x 22 bridge for indic translation The paper introduces a synthetic parallel translation dataset covering 22 scheduled Indic languages aligned via English pivot sentences. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Deals with natural language translation, not programming or code generation.
bharatSetu: a 1M x 22 bridge for indic translation The paper introduces a synthetic parallel translation dataset covering 22 scheduled Indic languages aligned via English pivot sentences. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Concerns translation dataset curation, not AI safety or alignment.
bharatSetu: a 1M x 22 bridge for indic translation The paper introduces a synthetic parallel translation dataset covering 22 scheduled Indic languages aligned via English pivot sentences. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Presents a dataset and translation pairs, not model compression.
bharatSetu: a 1M x 22 bridge for indic translation The paper introduces a synthetic parallel translation dataset covering 22 scheduled Indic languages aligned via English pivot sentences. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses purely on text translation, not multimodal text-and-image data.
bharatSetu: a 1M x 22 bridge for indic translation The paper introduces a synthetic parallel translation dataset covering 22 scheduled Indic languages aligned via English pivot sentences. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on parallel translation corpora, not retrieval-augmented generation.
bharatSetu: a 1M x 22 bridge for indic translation The paper introduces a synthetic parallel translation dataset covering 22 scheduled Indic languages aligned via English pivot sentences. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Introduces a machine translation corpus, not multi-step agents.
SpecForge: Recursive Self-Improvement for Coding Agents via Behavioral Specifications The paper presents a self-improvement method where coding agents refine natural-language behavioral specifications using execution-verified tests. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an agent improvement framework rather than a testing benchmark.
SpecForge: Recursive Self-Improvement for Coding Agents via Behavioral Specifications The paper presents a self-improvement method where coding agents refine natural-language behavioral specifications using execution-verified tests. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on code specification refinement, not AI safety or security.
What DNA Foundation Models Learn Beyond Sequence Composition The paper evaluates whether genomic foundation models offer predictive value over simple sequence frequency baselines across classification benchmarks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies biological sequence representations, not LLM evaluation methods.
What DNA Foundation Models Learn Beyond Sequence Composition The paper evaluates whether genomic foundation models offer predictive value over simple sequence frequency baselines across classification benchmarks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Analyzes genomic feature representations, not deployment safety or guardrails.
What DNA Foundation Models Learn Beyond Sequence Composition The paper evaluates whether genomic foundation models offer predictive value over simple sequence frequency baselines across classification benchmarks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Compares genomic representation features, not model compression or efficiency.
What DNA Foundation Models Learn Beyond Sequence Composition The paper evaluates whether genomic foundation models offer predictive value over simple sequence frequency baselines across classification benchmarks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates DNA foundation models, not multi-step autonomous agents.
History Replays Itself: Unleashing Long-Context Capability in Linear Attention The paper proposes a training-free episodic memory mechanism that extends linear attention language models to long-context tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces an architectural memory module rather than an evaluation benchmark.
History Replays Itself: Unleashing Long-Context Capability in Linear Attention The paper proposes a training-free episodic memory mechanism that extends linear attention language models to long-context tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on long-context modeling capacity, not AI safety or guardrails.
History Replays Itself: Unleashing Long-Context Capability in Linear Attention The paper proposes a training-free episodic memory mechanism that extends linear attention language models to long-context tasks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes an attention memory mechanism, not multi-step LLM agents.
Uncovering the Generative Landscape of Diffusion Models: A Spectral-Guided Quality-Diversity Diffusion Framework The paper integrates quality-diversity optimization with diffusion models to generate diverse sets of high-performing outputs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on output diversity in diffusion models, not efficient inference.
Uncovering the Generative Landscape of Diffusion Models: A Spectral-Guided Quality-Diversity Diffusion Framework The paper integrates quality-diversity optimization with diffusion models to generate diverse sets of high-performing outputs. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies diffusion sampling optimization, not multi-step language agents.
Uncovering the Generative Landscape of Diffusion Models: A Spectral-Guided Quality-Diversity Diffusion Framework The paper integrates quality-diversity optimization with diffusion models to generate diverse sets of high-performing outputs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Explores sample diversity in diffusion, not deployment safety or robustness.
Uncovering the Generative Landscape of Diffusion Models: A Spectral-Guided Quality-Diversity Diffusion Framework The paper integrates quality-diversity optimization with diffusion models to generate diverse sets of high-performing outputs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces an optimization algorithm for diffusion, not LLM evaluation.
SkillMarker: A KGW-Based Watermarking Method for Agent Skill Ownership Verification The paper introduces a watermarking method to verify ownership and trace unauthorized copying of agent skill instructions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on watermarking mechanisms rather than evaluation benchmark methodology.
Visible but Not Avoided: State-Dependent Re-Proposal of Rejected Candidates in Catalog-Based LLM Optimization Loops The paper demonstrates that language model optimizers frequently re-propose previously rejected candidates during search trajectories. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies optimization loop efficiency, not AI safety or guardrails.
Google Earth AI Geospatial Reasoning Agent: Capabilities and a Systematic Evaluation The paper presents Google Earth AI's geospatial reasoning agent along with evaluation benchmarks for complex multi-step geospatial workflows. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Presents geospatial agent workflows, not AI safety or alignment risks.
Hyperbolic Time Series Forecasting with Learnable Decomposition and Manifold Dynamics The paper proposes a hyperbolic time series forecasting framework that maps multiscale frequency hierarchies onto the Poincaré ball. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with time series forecasting accuracy, not AI deployment safety.
Hyperbolic Time Series Forecasting with Learnable Decomposition and Manifold Dynamics The paper proposes a hyperbolic time series forecasting framework that maps multiscale frequency hierarchies onto the Poincaré ball. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Proposes a geometric representation framework, not model compression or efficiency.
Hyperbolic Time Series Forecasting with Learnable Decomposition and Manifold Dynamics The paper proposes a hyperbolic time series forecasting framework that maps multiscale frequency hierarchies onto the Poincaré ball. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on time series forecasting models, not LLM agents.
Hyperbolic Time Series Forecasting with Learnable Decomposition and Manifold Dynamics The paper proposes a hyperbolic time series forecasting framework that maps multiscale frequency hierarchies onto the Poincaré ball. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a hyperbolic forecasting architecture, not an LLM evaluation benchmark.
A Decade in a Day: Synthesizing Agent-Native Interfaces from Human-Oriented Software Evolution The paper introduces a benchmark measuring how software interface versions affect the task performance of desktop language model agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on interface usability for agents, not safety or guardrails.
SurgiCLAW: An Interpretable Atomic-Skill Agent for Precision Robotic Micromanipulation The paper presents a hierarchical agent combining vision-language models for task planning with deterministic control programs for micromanipulation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Targets robotic execution precision rather than general enterprise AI deployment safety.
SurgiCLAW: An Interpretable Atomic-Skill Agent for Precision Robotic Micromanipulation The paper presents a hierarchical agent combining vision-language models for task planning with deterministic control programs for micromanipulation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on surgical robotic micromanipulation, not industrial manufacturing quality control.
SurgiCLAW: An Interpretable Atomic-Skill Agent for Precision Robotic Micromanipulation The paper presents a hierarchical agent combining vision-language models for task planning with deterministic control programs for micromanipulation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a surgical robotic framework rather than general LLM evaluation.
Agent Sociodrama: Social-Dynamics-Guided Persona Recasting for LLM Agent Teams The paper introduces a framework to model evolving social dynamics and update personas in multi-agent language model teams. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-based persona interactions, not multimodal text-image systems.
Agent Sociodrama: Social-Dynamics-Guided Persona Recasting for LLM Agent Teams The paper introduces a framework to model evolving social dynamics and update personas in multi-agent language model teams. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Investigates social persona dynamics, not software engineering or code generation.
Agent Sociodrama: Social-Dynamics-Guided Persona Recasting for LLM Agent Teams The paper introduces a framework to model evolving social dynamics and update personas in multi-agent language model teams. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an agent coordination method rather than an evaluation benchmark.
Agent Sociodrama: Social-Dynamics-Guided Persona Recasting for LLM Agent Teams The paper introduces a framework to model evolving social dynamics and update personas in multi-agent language model teams. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on multi-agent collaboration dynamics, not retrieval-augmented generation.
Agent Sociodrama: Social-Dynamics-Guided Persona Recasting for LLM Agent Teams The paper introduces a framework to model evolving social dynamics and update personas in multi-agent language model teams. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies persona recasting for performance, not deployment safety or jailbreak prevention.
Agent Sociodrama: Social-Dynamics-Guided Persona Recasting for LLM Agent Teams The paper introduces a framework to model evolving social dynamics and update personas in multi-agent language model teams. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not explore model compression, quantization, or efficient serving.
An Explainable System for Offensive Content Detection in Nastaliq Urdu: Corpus Curation, Transformer Benchmarking, and Word-Level Attribution The paper presents a Nastaliq Urdu comment corpus and evaluates transformer models with word-level attributions for offensive content detection. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with content moderation classification, not autonomous multi-step agents.
An Explainable System for Offensive Content Detection in Nastaliq Urdu: Corpus Curation, Transformer Benchmarking, and Word-Level Attribution The paper presents a Nastaliq Urdu comment corpus and evaluates transformer models with word-level attributions for offensive content detection. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses solely on text classification in Urdu, not multimodal models.
An Explainable System for Offensive Content Detection in Nastaliq Urdu: Corpus Curation, Transformer Benchmarking, and Word-Level Attribution The paper presents a Nastaliq Urdu comment corpus and evaluates transformer models with word-level attributions for offensive content detection. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Concerns natural language moderation, not coding or software engineering.
An Explainable System for Offensive Content Detection in Nastaliq Urdu: Corpus Curation, Transformer Benchmarking, and Word-Level Attribution The paper presents a Nastaliq Urdu comment corpus and evaluates transformer models with word-level attributions for offensive content detection. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on text classification, not retrieval-augmented generation architectures.
An Explainable System for Offensive Content Detection in Nastaliq Urdu: Corpus Curation, Transformer Benchmarking, and Word-Level Attribution The paper presents a Nastaliq Urdu comment corpus and evaluates transformer models with word-level attributions for offensive content detection. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Compares standard transformer classifiers without studying model compression or efficiency.
Passing Tests, Missing Semantics: Evidence of Interpretive Lock-In in Plan-Based DataFrame Code Generation The paper examines interpretive lock-in where planning agents generate code passing self-generated tests while missing the actual specification requirements. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on semantic code correctness rather than safety, guardrails, or jailbreaks.
Passing Tests, Missing Semantics: Evidence of Interpretive Lock-In in Plan-Based DataFrame Code Generation The paper examines interpretive lock-in where planning agents generate code passing self-generated tests while missing the actual specification requirements. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies prompt planning and testing in code generation, not agent architectures.
Adaptive Look, Flexible Thought: Learning How and How Long to Think in Multimodal Reasoning The paper proposes a reasoning framework that discretizes visual states into tokens to jointly optimize visual computation and chain-of-thought steps. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies multimodal internal reasoning depth rather than autonomous multi-step software agents.

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