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
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.
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 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	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	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	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Text-only language model work, not multimodal.
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.
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.
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.
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.
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	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	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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses multilingual pretraining rather than model compression or inference efficiency.
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.
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 safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Analyzes recurrent neural network dynamics, not deployment safety risks.
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.
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.
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	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 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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes state representations in agents, not general evaluation benchmarks.
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.
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	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	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	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 agents via arxiv · 15 Oct, 06:17	Not relevant Addresses speculative decoding for MoE serving, not LLM agents.
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.
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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on offline meta-reinforcement learning, not language model 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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an RL optimization algorithm, not an evaluation benchmark.
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.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses reinforcement learning distribution shifts, not deployment safety guardrails.
World Modeling through Spectral Alignment This paper introduces a world-modeling framework that uses kernel similarity to control which physical properties are preserved in learned embeddings. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses representation geometry, not model compression or efficient serving.
World Modeling through Spectral Alignment This paper introduces a world-modeling framework that uses kernel similarity to control which physical properties are preserved in learned embeddings. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on representation learning in world models, not LLM agents.
World Modeling through Spectral Alignment This paper introduces a world-modeling framework that uses kernel similarity to control which physical properties are preserved in learned embeddings. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Investigates representation learning, not deployment safety or reliability risks.
World Modeling through Spectral Alignment This paper introduces a world-modeling framework that uses kernel similarity to control which physical properties are preserved in learned embeddings. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a representation learning method, not an evaluation benchmark.
Hidden in Plain Sight: Advancing Harmful Content Understanding via Perception-Aware Adversarial Self-Play This paper presents an adversarial self-play framework using multi-perceptual views to improve language model detection of obfuscated harmful text. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model compression, quantization, or distillation.
Hidden in Plain Sight: Advancing Harmful Content Understanding via Perception-Aware Adversarial Self-Play This paper presents an adversarial self-play framework using multi-perceptual views to improve language model detection of obfuscated harmful text. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on safety evasion evasion rather than core multimodal reasoning.
Hidden in Plain Sight: Advancing Harmful Content Understanding via Perception-Aware Adversarial Self-Play This paper presents an adversarial self-play framework using multi-perceptual views to improve language model detection of obfuscated harmful text. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation from external documents.
Hidden in Plain Sight: Advancing Harmful Content Understanding via Perception-Aware Adversarial Self-Play This paper presents an adversarial self-play framework using multi-perceptual views to improve language model detection of obfuscated harmful text. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not address code generation or software engineering tasks.
Hidden in Plain Sight: Advancing Harmful Content Understanding via Perception-Aware Adversarial Self-Play This paper presents an adversarial self-play framework using multi-perceptual views to improve language model detection of obfuscated harmful text. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces an adversarial training method, not a general evaluation benchmark.
Hidden in Plain Sight: Advancing Harmful Content Understanding via Perception-Aware Adversarial Self-Play This paper presents an adversarial self-play framework using multi-perceptual views to improve language model detection of obfuscated harmful text. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on adversarial content moderation, not interactive tool-use agents.
BridgeCodec: Mamba Enhanced Neural Audio Codec with Schrödinger Bridge at Low Bitrate This paper proposes an optimal-transport bridge framework to translate audio representations directly between mismatched neural codecs at low bitrates. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on neural audio codecs, not language model agents.
BridgeCodec: Mamba Enhanced Neural Audio Codec with Schrödinger Bridge at Low Bitrate This paper proposes an optimal-transport bridge framework to translate audio representations directly between mismatched neural codecs at low bitrates. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses audio cross-codec translation, not AI deployment safety.
BridgeCodec: Mamba Enhanced Neural Audio Codec with Schrödinger Bridge at Low Bitrate This paper proposes an optimal-transport bridge framework to translate audio representations directly between mismatched neural codecs at low bitrates. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces an audio translation architecture, not an evaluation benchmark.
When Messages Go Wrong: Understanding Communication Faults in LLM-Based Multi-Agent Systems This paper provides an empirical study and taxonomy of communication faults and error propagation across multi-agent language model systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates system communication faults rather than LLM benchmarking standards.
When Messages Go Wrong: Understanding Communication Faults in LLM-Based Multi-Agent Systems This paper provides an empirical study and taxonomy of communication faults and error propagation across multi-agent language model systems. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies multi-agent communication networks rather than software coding models.
Scalable Robust MCTS with Anytime Deterministic Optimality Guarantees This paper develops an online tree search method providing deterministic anytime bounds for robust planning under uncertain transition dynamics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses classic MDP transition uncertainty, not deployed AI safety guardrails.
Scalable Robust MCTS with Anytime Deterministic Optimality Guarantees This paper develops an online tree search method providing deterministic anytime bounds for robust planning under uncertain transition dynamics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an optimization algorithm, not an evaluation benchmark.

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