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
StimFlow: Synthesizing Brain Activity from Images via Stimulus-conditioned Flow Matching The paper presents StimFlow, using conditional flow matching to synthesize fMRI brain activity directly in voxel space from visual stimuli. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Synthesizes fMRI signals from images, not LLM agent systems.
StimFlow: Synthesizing Brain Activity from Images via Stimulus-conditioned Flow Matching The paper presents StimFlow, using conditional flow matching to synthesize fMRI brain activity directly in voxel space from visual stimuli. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates neural response synthesis, not general LLM benchmarking.
Across Viewpoints and Time: Trajectory-Generalizable Physical Attacks on World Models and Video Understanding Models The paper develops an attack framework to generate physical adversarial textures that deceive video models across varying trajectories and viewpoints. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Concerns video/world models rather than industrial inspection or manufacturing defect detection.
Across Viewpoints and Time: Trajectory-Generalizable Physical Attacks on World Models and Video Understanding Models The paper develops an attack framework to generate physical adversarial textures that deceive video models across varying trajectories and viewpoints. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an adversarial attack method rather than standard evaluation methodologies.
Across Viewpoints and Time: Trajectory-Generalizable Physical Attacks on World Models and Video Understanding Models The paper develops an attack framework to generate physical adversarial textures that deceive video models across varying trajectories and viewpoints. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies physical visual adversarial attacks, not LLM agents.
CodeWalk: Generating Coding Benchmarks by Walking a Problem Graph The paper presents CodeWalk, a method to create challenging coding benchmarks at low cost by programmatically composing verified atomic problems. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Generates synthetic programming problems rather than planning agent systems.
CodeWalk: Generating Coding Benchmarks by Walking a Problem Graph The paper presents CodeWalk, a method to create challenging coding benchmarks at low cost by programmatically composing verified atomic problems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on programming task generation rather than safety guardrails or robustness.
CodeWalk: Generating Coding Benchmarks by Walking a Problem Graph The paper presents CodeWalk, a method to create challenging coding benchmarks at low cost by programmatically composing verified atomic problems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Discusses benchmark composition rather than model quantization or inference efficiency.
Gugak-VocalSet: A Multi-Genre Studio-Quality Dataset of Korean Traditional Singing with Sigimsae Annotations The paper releases a studio-quality audio dataset of Korean traditional singing with dense annotations for vocal expressions and melodic contour. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a traditional Korean music audio dataset, not LLM benchmarks.
Gugak-VocalSet: A Multi-Genre Studio-Quality Dataset of Korean Traditional Singing with Sigimsae Annotations The paper releases a studio-quality audio dataset of Korean traditional singing with dense annotations for vocal expressions and melodic contour. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Concerns music transcription, not AI safety or deployment risks.
Gugak-VocalSet: A Multi-Genre Studio-Quality Dataset of Korean Traditional Singing with Sigimsae Annotations The paper releases a studio-quality audio dataset of Korean traditional singing with dense annotations for vocal expressions and melodic contour. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on traditional vocal audio datasets, not AI agents.
Siam-ALS: An Explainable Siamese Deep Learning Framework for Early ALS Detection Using EEG and Eye-Tracking Signals The paper introduces an explainable Siamese deep learning framework combining synchronized EEG and eye-tracking signals for early ALS detection. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Applies neural networks to medical diagnostics, not LLM agents.
Siam-ALS: An Explainable Siamese Deep Learning Framework for Early ALS Detection Using EEG and Eye-Tracking Signals The paper introduces an explainable Siamese deep learning framework combining synchronized EEG and eye-tracking signals for early ALS detection. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers biomedical classification rather than AI system safety and reliability.
Siam-ALS: An Explainable Siamese Deep Learning Framework for Early ALS Detection Using EEG and Eye-Tracking Signals The paper introduces an explainable Siamese deep learning framework combining synchronized EEG and eye-tracking signals for early ALS detection. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates a clinical model, not LLM evaluation frameworks.
Siam-ALS: An Explainable Siamese Deep Learning Framework for Early ALS Detection Using EEG and Eye-Tracking Signals The paper introduces an explainable Siamese deep learning framework combining synchronized EEG and eye-tracking signals for early ALS detection. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or efficient LLM serving.
Adversarial Detection for Pre-trained Classifiers via Lipschitz-Margin Analysis with Certification The paper proposes certified adversarial detection using Lipschitz-margin analysis to detect perturbations rather than forcing invariant predictions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on certified adversarial detection methods, not general evaluation benchmarks.
Adversarial Detection for Pre-trained Classifiers via Lipschitz-Margin Analysis with Certification The paper proposes certified adversarial detection using Lipschitz-margin analysis to detect perturbations rather than forcing invariant predictions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies vision classifier certification, not agent planning or tool use.
Adversarial Detection for Pre-trained Classifiers via Lipschitz-Margin Analysis with Certification The paper proposes certified adversarial detection using Lipschitz-margin analysis to detect perturbations rather than forcing invariant predictions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Presents theoretical robustness bounds without focusing on compression or serving efficiency.
TRACE: Temporal Risk Assessment and Concept Editing for Safer Language Generation The paper introduces TRACE to monitor harmful response risks during generation, edit token activations selectively, and maintain auditable decision traces. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses text-generation guardrails, not autonomous multi-step agents.
TRACE: Temporal Risk Assessment and Concept Editing for Safer Language Generation The paper introduces TRACE to monitor harmful response risks during generation, edit token activations selectively, and maintain auditable decision traces. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on risk mitigation rather than model compression or inference efficiency.
TRACE: Temporal Risk Assessment and Concept Editing for Safer Language Generation The paper introduces TRACE to monitor harmful response risks during generation, edit token activations selectively, and maintain auditable decision traces. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a safety steering framework rather than a general evaluation benchmark.
A Sovereign, Indic-First, Reasoning Model The paper presents a hybrid language model adapted for reasoning, instruction following, and tool use across English and eight Indian languages. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Reports benchmark performance without investigating evaluation methodology.
A Sovereign, Indic-First, Reasoning Model The paper presents a hybrid language model adapted for reasoning, instruction following, and tool use across English and eight Indian languages. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on pretraining and post-training, not RAG.
A Sovereign, Indic-First, Reasoning Model The paper presents a hybrid language model adapted for reasoning, instruction following, and tool use across English and eight Indian languages. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant General reasoning model rather than a specialized code assistant.
A Sovereign, Indic-First, Reasoning Model The paper presents a hybrid language model adapted for reasoning, instruction following, and tool use across English and eight Indian languages. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers foundation model training rather than agent frameworks.
A Sovereign, Indic-First, Reasoning Model The paper presents a hybrid language model adapted for reasoning, instruction following, and tool use across English and eight Indian languages. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on multilingual reasoning rather than AI deployment safety.
A Sovereign, Indic-First, Reasoning Model The paper presents a hybrid language model adapted for reasoning, instruction following, and tool use across English and eight Indian languages. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with a 30B MoE pretraining rather than efficient model compression.
A Sovereign, Indic-First, Reasoning Model The paper presents a hybrid language model adapted for reasoning, instruction following, and tool use across English and eight Indian languages. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Text-only language model work, not multimodal.
Fewer Bits, More Tries: Recovering Capability Under Memory Constraints This paper shows that running multiple parallel attempts with a smaller model can match the coding accuracy of much larger models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies test-time search trade-offs, not safety, guardrails, or alignment.
Fewer Bits, More Tries: Recovering Capability Under Memory Constraints This paper shows that running multiple parallel attempts with a smaller model can match the coding accuracy of much larger models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Examines parallel test-time generation, not autonomous multi-step agents.
Fewer Bits, More Tries: Recovering Capability Under Memory Constraints This paper shows that running multiple parallel attempts with a smaller model can match the coding accuracy of much larger models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates on standard benchmarks rather than analyzing benchmark methodology.
In-Place Indic Tokenizer Expansion and Continual Pretraining of Nemotron-3-Nano-30B-A3B This paper details vocabulary expansion and continual pretraining techniques to adapt an open language model across eight Indic languages. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval or external knowledge grounding.
In-Place Indic Tokenizer Expansion and Continual Pretraining of Nemotron-3-Nano-30B-A3B This paper details vocabulary expansion and continual pretraining techniques to adapt an open language model across eight Indic languages. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses multilingual pretraining rather than model compression or inference efficiency.
In-Place Indic Tokenizer Expansion and Continual Pretraining of Nemotron-3-Nano-30B-A3B This paper details vocabulary expansion and continual pretraining techniques to adapt an open language model across eight Indic languages. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on tokenizer expansion and continual pretraining, not agent workflows.
In-Place Indic Tokenizer Expansion and Continual Pretraining of Nemotron-3-Nano-30B-A3B This paper details vocabulary expansion and continual pretraining techniques to adapt an open language model across eight Indic languages. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on natural language pretraining rather than code generation.
In-Place Indic Tokenizer Expansion and Continual Pretraining of Nemotron-3-Nano-30B-A3B This paper details vocabulary expansion and continual pretraining techniques to adapt an open language model across eight Indic languages. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates on benchmarks but does not introduce evaluation methodologies.
In-Place Indic Tokenizer Expansion and Continual Pretraining of Nemotron-3-Nano-30B-A3B This paper details vocabulary expansion and continual pretraining techniques to adapt an open language model across eight Indic languages. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers language pretraining without evaluating deployment safety or guardrails.
In-Place Indic Tokenizer Expansion and Continual Pretraining of Nemotron-3-Nano-30B-A3B This paper details vocabulary expansion and continual pretraining techniques to adapt an open language model across eight Indic languages. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Text-only language model work with no multimodal inputs.
Analyzing the Dynamics of WSBM-based Echo State Networks using InputDSA This paper analyzes the computational dynamics of biologically structured modular reservoir networks using a comparative dynamic similarity framework. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Analyzes recurrent neural network dynamics, not deployment safety risks.
Analyzing the Dynamics of WSBM-based Echo State Networks using InputDSA This paper analyzes the computational dynamics of biologically structured modular reservoir networks using a comparative dynamic similarity framework. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies reservoir computing dynamics, not evaluation benchmark methodologies.
Analyzing the Dynamics of WSBM-based Echo State Networks using InputDSA This paper analyzes the computational dynamics of biologically structured modular reservoir networks using a comparative dynamic similarity framework. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates biological dynamics in Echo State Networks, not LLM agents.
E8 Lattice Key Quantization for Low-Bit KV-Cache Compression This paper presents a lattice vector quantization method that compresses attention key-value caches to two bits while preserving model quality. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on cache compression techniques, not deployment safety.
E8 Lattice Key Quantization for Low-Bit KV-Cache Compression This paper presents a lattice vector quantization method that compresses attention key-value caches to two bits while preserving model quality. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on KV cache quantization, not LLM agent architectures.
E8 Lattice Key Quantization for Low-Bit KV-Cache Compression This paper presents a lattice vector quantization method that compresses attention key-value caches to two bits while preserving model quality. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a compression technique, not a general evaluation benchmark.
A Decade of PhotoMappers: A Longitudinal Study of Crowdsourced Disaster Photos Geolocalization with Vision-Language Models and Geospatial Reasoning This study analyzes a decade of crowdsourced disaster photos to evaluate vision-language models against human geographic reasoning for location estimation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on disaster image geolocalization, not LLM agent systems.
A Decade of PhotoMappers: A Longitudinal Study of Crowdsourced Disaster Photos Geolocalization with Vision-Language Models and Geospatial Reasoning This study analyzes a decade of crowdsourced disaster photos to evaluate vision-language models against human geographic reasoning for location estimation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on geospatial disaster mapping, not industrial defect detection.
A Decade of PhotoMappers: A Longitudinal Study of Crowdsourced Disaster Photos Geolocalization with Vision-Language Models and Geospatial Reasoning This study analyzes a decade of crowdsourced disaster photos to evaluate vision-language models against human geographic reasoning for location estimation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on image geolocalization, not safety or guardrails.
A Decade of PhotoMappers: A Longitudinal Study of Crowdsourced Disaster Photos Geolocalization with Vision-Language Models and Geospatial Reasoning This study analyzes a decade of crowdsourced disaster photos to evaluate vision-language models against human geographic reasoning for location estimation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a domain-specific study, not a general evaluation benchmark.
State Representation Shapes Long-Horizon Agent Learning and Transfer: An Empirical Study This empirical study evaluates how compact versus full state representations affect the ability of language model agents to learn long-horizon workflows. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Examines workflow state representations, not safety or guardrail risks.
State Representation Shapes Long-Horizon Agent Learning and Transfer: An Empirical Study This empirical study evaluates how compact versus full state representations affect the ability of language model agents to learn long-horizon workflows. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes state representations in agents, not general evaluation benchmarks.
CoverSynth: Coverage-Guided Generation of Retrieval Evaluation Sets This paper introduces a synthetic data generation and assessment framework designed to produce comprehensive evaluation benchmarks for enterprise retrieval systems. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on generating retrieval test sets, not agent planning.
CoverSynth: Coverage-Guided Generation of Retrieval Evaluation Sets This paper introduces a synthetic data generation and assessment framework designed to produce comprehensive evaluation benchmarks for enterprise retrieval systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses evaluation set generation rather than safety guardrails.
Sensitivity or Criterion? Measuring and Moving When Language Models Act This paper introduces a diagnostic framework using signal detection theory to measure when language model agents decide to use tools. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Evaluates physical state tool actions, not software code generation.
Sensitivity or Criterion? Measuring and Moving When Language Models Act This paper introduces a diagnostic framework using signal detection theory to measure when language model agents decide to use tools. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on tool-use decision boundaries rather than retrieval augmentation.
Sensitivity or Criterion? Measuring and Moving When Language Models Act This paper introduces a diagnostic framework using signal detection theory to measure when language model agents decide to use tools. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-based tool decisions, not multimodal models.
Sensitivity or Criterion? Measuring and Moving When Language Models Act This paper introduces a diagnostic framework using signal detection theory to measure when language model agents decide to use tools. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Analyzes model sensitivity and criterion, not compression or efficiency.
SpecFit: Not All Draft Tokens Cost the Same in Offloaded MoE Inference This paper proposes a speculative decoding tree selection strategy that accounts for host-to-device transfer costs during offloaded mixture-of-experts inference. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on inference speedup rather than deployment safety risks.
SpecFit: Not All Draft Tokens Cost the Same in Offloaded MoE Inference This paper proposes a speculative decoding tree selection strategy that accounts for host-to-device transfer costs during offloaded mixture-of-experts inference. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Optimizes inference throughput rather than model evaluation benchmarks.
SpecFit: Not All Draft Tokens Cost the Same in Offloaded MoE Inference This paper proposes a speculative decoding tree selection strategy that accounts for host-to-device transfer costs during offloaded mixture-of-experts inference. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses speculative decoding for MoE serving, not LLM agents.
Pessimistic Latent Task-aware Optimization for Robust Offline Meta-Reinforcement Learning This paper proposes a pessimistic offline meta-reinforcement learning method that generates synthetic off-support latents to improve out-of-distribution task transfer. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL latent representations, not model compression.

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