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
SITAR: A Situational Distortion Metric for Automatic Speech Recognition Evaluation Develops a lightweight situational distortion metric to evaluate speech recognition errors based on practical conversational severity. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Deals with speech recognition evaluation, not programming or code generation.
SITAR: A Situational Distortion Metric for Automatic Speech Recognition Evaluation Develops a lightweight situational distortion metric to evaluate speech recognition errors based on practical conversational severity. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on transcription scoring accuracy rather than deployment safety or jailbreak prevention.
Translation Circuit Discovery and Targeted Optimization for Low-Resource Multilingual Translation Identifies translation circuits in multilingual models and optimizes fewer than five percent of parameters for low-resource translation. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on translation circuits, not multi-step agent planning or tools.
Translation Circuit Discovery and Targeted Optimization for Low-Resource Multilingual Translation Identifies translation circuits in multilingual models and optimizes fewer than five percent of parameters for low-resource translation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Studies internal translation circuits rather than external knowledge retrieval.
Translation Circuit Discovery and Targeted Optimization for Low-Resource Multilingual Translation Identifies translation circuits in multilingual models and optimizes fewer than five percent of parameters for low-resource translation. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Applies to text-based multilingual translation without any multimodal components.
Translation Circuit Discovery and Targeted Optimization for Low-Resource Multilingual Translation Identifies translation circuits in multilingual models and optimizes fewer than five percent of parameters for low-resource translation. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Investigates mechanistic interpretability for translation without focusing on safety or jailbreaks.
Translation Circuit Discovery and Targeted Optimization for Low-Resource Multilingual Translation Identifies translation circuits in multilingual models and optimizes fewer than five percent of parameters for low-resource translation. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on natural language translation, not code generation or software engineering.
Translation Circuit Discovery and Targeted Optimization for Low-Resource Multilingual Translation Identifies translation circuits in multilingual models and optimizes fewer than five percent of parameters for low-resource translation. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on translation optimization rather than LLM evaluation methodology or benchmark flaws.
OPRD: On-Policy Reminder Distillation for Long-Horizon Coding Agents Uses multi-scope reminders and on-policy distillation to help coding agents maintain constraints over long software engineering interactions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on agent trajectory supervision rather than external document retrieval and grounding.
OPRD: On-Policy Reminder Distillation for Long-Horizon Coding Agents Uses multi-scope reminders and on-policy distillation to help coding agents maintain constraints over long software engineering interactions. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on text-based code repositories, not multimodal models.
OPRD: On-Policy Reminder Distillation for Long-Horizon Coding Agents Uses multi-scope reminders and on-policy distillation to help coding agents maintain constraints over long software engineering interactions. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on coding task performance rather than safety, guardrails, or alignment risks.
OPRD: On-Policy Reminder Distillation for Long-Horizon Coding Agents Uses multi-scope reminders and on-policy distillation to help coding agents maintain constraints over long software engineering interactions. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Uses distillation for agent training, not for model compression or efficient serving.
OPRD: On-Policy Reminder Distillation for Long-Horizon Coding Agents Uses multi-scope reminders and on-policy distillation to help coding agents maintain constraints over long software engineering interactions. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Uses coding benchmarks for evaluation rather than proposing new LLM benchmark methodologies.
Writability and the Pathway: A Controlled Study of Knowledge Injection into Addressed Memory Conducts a controlled evaluation of direct knowledge injection into addressed conditional memory modules across small and production models. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Deals strictly with text-based language models, not multimodal inputs.
Writability and the Pathway: A Controlled Study of Knowledge Injection into Addressed Memory Conducts a controlled evaluation of direct knowledge injection into addressed conditional memory modules across small and production models. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on memory module knowledge injection, not multi-step agent planning or tools.
Writability and the Pathway: A Controlled Study of Knowledge Injection into Addressed Memory Conducts a controlled evaluation of direct knowledge injection into addressed conditional memory modules across small and production models. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Tests post-hoc parameter updates rather than model compression, quantization, or distillation.
Writability and the Pathway: A Controlled Study of Knowledge Injection into Addressed Memory Conducts a controlled evaluation of direct knowledge injection into addressed conditional memory modules across small and production models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Studies parametric memory updates rather than retrieval-augmented generation from external databases.
Writability and the Pathway: A Controlled Study of Knowledge Injection into Addressed Memory Conducts a controlled evaluation of direct knowledge injection into addressed conditional memory modules across small and production models. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on knowledge editing in language models, not code generation or repair.
Writability and the Pathway: A Controlled Study of Knowledge Injection into Addressed Memory Conducts a controlled evaluation of direct knowledge injection into addressed conditional memory modules across small and production models. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Investigates internal memory writing mechanisms rather than deployment safety, jailbreaks, or guardrails.
Writability and the Pathway: A Controlled Study of Knowledge Injection into Addressed Memory Conducts a controlled evaluation of direct knowledge injection into addressed conditional memory modules across small and production models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Evaluates parametric memory writing techniques rather than general LLM benchmark reliability.
Waypoint-OPD: Transferring RL-Induced Policy Shifts via Gated On-Policy Distillation Gates reinforcement learning policy-shift distillation using intermediate checkpoints to ensure student models only learn consistent teacher shifts. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Proposes an RL distillation algorithm, not autonomous planning agents.
Waypoint-OPD: Transferring RL-Induced Policy Shifts via Gated On-Policy Distillation Gates reinforcement learning policy-shift distillation using intermediate checkpoints to ensure student models only learn consistent teacher shifts. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Introduces a distillation algorithm rather than an evaluation benchmark.
Waypoint-OPD: Transferring RL-Induced Policy Shifts via Gated On-Policy Distillation Gates reinforcement learning policy-shift distillation using intermediate checkpoints to ensure student models only learn consistent teacher shifts. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Addresses policy distillation performance rather than safety guardrails or attacks.
Waypoint-OPD: Transferring RL-Induced Policy Shifts via Gated On-Policy Distillation Gates reinforcement learning policy-shift distillation using intermediate checkpoints to ensure student models only learn consistent teacher shifts. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on mathematical and code text reasoning, not multimodal data.
Waypoint-OPD: Transferring RL-Induced Policy Shifts via Gated On-Policy Distillation Gates reinforcement learning policy-shift distillation using intermediate checkpoints to ensure student models only learn consistent teacher shifts. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation or grounding.
ContextLift: Breaking the Spurious Correlation Behind Conditional Emergent Misalignment Introduces a data augmentation method that breaks spurious contextual correlations causing emergent misalignment during domain finetuning. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Investigates safety alignment across domains, not code generation.
ContextLift: Breaking the Spurious Correlation Behind Conditional Emergent Misalignment Introduces a data augmentation method that breaks spurious contextual correlations causing emergent misalignment during domain finetuning. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not involve model quantization, pruning, or efficient serving.
ContextLift: Breaking the Spurious Correlation Behind Conditional Emergent Misalignment Introduces a data augmentation method that breaks spurious contextual correlations causing emergent misalignment during domain finetuning. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on alignment fine-tuning, not autonomous tool-using agents.
ContextLift: Breaking the Spurious Correlation Behind Conditional Emergent Misalignment Introduces a data augmentation method that breaks spurious contextual correlations causing emergent misalignment during domain finetuning. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Evaluates text language models without image, video, or audio data.
ContextLift: Breaking the Spurious Correlation Behind Conditional Emergent Misalignment Introduces a data augmentation method that breaks spurious contextual correlations causing emergent misalignment during domain finetuning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not examine retrieval-augmented generation architectures.
ContextLift: Breaking the Spurious Correlation Behind Conditional Emergent Misalignment Introduces a data augmentation method that breaks spurious contextual correlations causing emergent misalignment during domain finetuning. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on fine-tuning alignment defense rather than evaluation benchmarking.
Spurious Attention Heads in Vision Transformers: Mechanistic Identification of Annotation-Driven Bias in Medical Chest X-Ray Analysis Identifies a specific vision transformer attention head that spuriously attends to medical image corner text instead of lung pathology. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on mechanistic interpretability rather than LLM benchmarking.
Spurious Attention Heads in Vision Transformers: Mechanistic Identification of Annotation-Driven Bias in Medical Chest X-Ray Analysis Identifies a specific vision transformer attention head that spuriously attends to medical image corner text instead of lung pathology. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Applies to medical image analysis, not software engineering or code.
Spurious Attention Heads in Vision Transformers: Mechanistic Identification of Annotation-Driven Bias in Medical Chest X-Ray Analysis Identifies a specific vision transformer attention head that spuriously attends to medical image corner text instead of lung pathology. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation or external document grounding.
Spurious Attention Heads in Vision Transformers: Mechanistic Identification of Annotation-Driven Bias in Medical Chest X-Ray Analysis Identifies a specific vision transformer attention head that spuriously attends to medical image corner text instead of lung pathology. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Analyzes vision transformer attention heads, not autonomous AI agents.
Spurious Attention Heads in Vision Transformers: Mechanistic Identification of Annotation-Driven Bias in Medical Chest X-Ray Analysis Identifies a specific vision transformer attention head that spuriously attends to medical image corner text instead of lung pathology. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not address model compression, pruning, or efficient serving.
Spurious Attention Heads in Vision Transformers: Mechanistic Identification of Annotation-Driven Bias in Medical Chest X-Ray Analysis Identifies a specific vision transformer attention head that spuriously attends to medical image corner text instead of lung pathology. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Studies medical chest X-rays, not industrial manufacturing or defect inspection.
When Does "Post-Call" Begin? An Availability Contract for Earnings-Call NLP Benchmarks Proposes an availability framework to identify and resolve within-event timing mismatches in earnings-call financial NLP benchmarks. Peer-reviewed	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on financial call transcripts, not software engineering code.
When Does "Post-Call" Begin? An Availability Contract for Earnings-Call NLP Benchmarks Proposes an availability framework to identify and resolve within-event timing mismatches in earnings-call financial NLP benchmarks. Peer-reviewed	AI agents via arxiv · 17 Oct, 06:17	Not relevant Deals with financial disclosure timing, not agent tool use or planning.
When Does "Post-Call" Begin? An Availability Contract for Earnings-Call NLP Benchmarks Proposes an availability framework to identify and resolve within-event timing mismatches in earnings-call financial NLP benchmarks. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not focus on retrieval-augmented generation architectures.
When Does "Post-Call" Begin? An Availability Contract for Earnings-Call NLP Benchmarks Proposes an availability framework to identify and resolve within-event timing mismatches in earnings-call financial NLP benchmarks. Peer-reviewed	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not cover model quantization, pruning, or efficient inference.
When Does "Post-Call" Begin? An Availability Contract for Earnings-Call NLP Benchmarks Proposes an availability framework to identify and resolve within-event timing mismatches in earnings-call financial NLP benchmarks. Peer-reviewed	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on financial benchmark data timing rather than multimodal modeling.
Enhancing Multimodal RLVR with Pairwise Token Contrast and Adaptive Trajectory Confidence Improves reinforcement learning credit assignment in multimodal reasoning using pairwise token contrast and adaptive trajectory confidence. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on reasoning accuracy rather than deployment safety or jailbreak prevention.
Enhancing Multimodal RLVR with Pairwise Token Contrast and Adaptive Trajectory Confidence Improves reinforcement learning credit assignment in multimodal reasoning using pairwise token contrast and adaptive trajectory confidence. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Does not target software engineering or source code generation.
Enhancing Multimodal RLVR with Pairwise Token Contrast and Adaptive Trajectory Confidence Improves reinforcement learning credit assignment in multimodal reasoning using pairwise token contrast and adaptive trajectory confidence. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not focus on model compression, quantization, or serving efficiency.
Enhancing Multimodal RLVR with Pairwise Token Contrast and Adaptive Trajectory Confidence Improves reinforcement learning credit assignment in multimodal reasoning using pairwise token contrast and adaptive trajectory confidence. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on training reward credit assignment, not multi-step agent actions.
Enhancing Multimodal RLVR with Pairwise Token Contrast and Adaptive Trajectory Confidence Improves reinforcement learning credit assignment in multimodal reasoning using pairwise token contrast and adaptive trajectory confidence. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not involve external document retrieval or grounding.
Enhancing Multimodal RLVR with Pairwise Token Contrast and Adaptive Trajectory Confidence Improves reinforcement learning credit assignment in multimodal reasoning using pairwise token contrast and adaptive trajectory confidence. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Develops an RLVR training technique rather than benchmarking methodology.
Diffusion Language Models as Open Quantum Systems Frames diffusion language models as open quantum systems to enable text generation conditioned on superpositions of prompts and thoughts. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Focuses on sampling formulations rather than model compression or efficient inference.
Diffusion Language Models as Open Quantum Systems Frames diffusion language models as open quantum systems to enable text generation conditioned on superpositions of prompts and thoughts. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Presents a novel text generation model, not an evaluation benchmark.
Diffusion Language Models as Open Quantum Systems Frames diffusion language models as open quantum systems to enable text generation conditioned on superpositions of prompts and thoughts. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on general diffusion text models, not software code tasks.
Diffusion Language Models as Open Quantum Systems Frames diffusion language models as open quantum systems to enable text generation conditioned on superpositions of prompts and thoughts. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not address retrieval-augmented generation or external document grounding.
Diffusion Language Models as Open Quantum Systems Frames diffusion language models as open quantum systems to enable text generation conditioned on superpositions of prompts and thoughts. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Proposes a quantum formulation of diffusion text generation, not agents.
Diffusion Language Models as Open Quantum Systems Frames diffusion language models as open quantum systems to enable text generation conditioned on superpositions of prompts and thoughts. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Applies diffusion models solely to text generation tasks.
Diffusion Language Models as Open Quantum Systems Frames diffusion language models as open quantum systems to enable text generation conditioned on superpositions of prompts and thoughts. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Does not investigate AI safety guardrails, attacks, or robustness.
No Single Tokenizer Feature Reliably Predicts Downstream Language Model Performance Shows across 200 models that proposed intrinsic tokenizer metrics do not reliably predict downstream language model performance. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Investigates text tokenizers without visual or auditory multimodal components.
No Single Tokenizer Feature Reliably Predicts Downstream Language Model Performance Shows across 200 models that proposed intrinsic tokenizer metrics do not reliably predict downstream language model performance. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Studies tokenizer metrics, not autonomous planning agent architectures.
No Single Tokenizer Feature Reliably Predicts Downstream Language Model Performance Shows across 200 models that proposed intrinsic tokenizer metrics do not reliably predict downstream language model performance. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Studies natural language tokenizers, not code-specific generation.
No Single Tokenizer Feature Reliably Predicts Downstream Language Model Performance Shows across 200 models that proposed intrinsic tokenizer metrics do not reliably predict downstream language model performance. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Analyzes tokenizer predictability, not safety vulnerabilities or jailbreaks.
No Single Tokenizer Feature Reliably Predicts Downstream Language Model Performance Shows across 200 models that proposed intrinsic tokenizer metrics do not reliably predict downstream language model performance. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not address external information retrieval or knowledge grounding.

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