Every paper checked — nothing hidden
All papers
Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.
All17,313In brief24More (on-topic)3,333Not relevant13,754Not assessed87Older352Could not confirm93Blogs with no paper266All blogs284
| Paper | Topic | Relevance | Feedback |
|---|---|---|---|
| From Candidate Tokens to Final Outcomes: Decoding Relative Long-Horizon Candidate Value from Reasoning Model Representations The paper constructs a dataset to show that relative candidate token value in multi-step reasoning is linearly decodable from model representations. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Studies mathematical reasoning tokens rather than software development. | |
| From Candidate Tokens to Final Outcomes: Decoding Relative Long-Horizon Candidate Value from Reasoning Model Representations The paper constructs a dataset to show that relative candidate token value in multi-step reasoning is linearly decodable from model representations. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Analyzes internal representations in reasoning models, not agent systems. | |
| From Candidate Tokens to Final Outcomes: Decoding Relative Long-Horizon Candidate Value from Reasoning Model Representations The paper constructs a dataset to show that relative candidate token value in multi-step reasoning is linearly decodable from model representations. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Investigates internal representation probing rather than evaluation benchmark integrity. | |
| From Candidate Tokens to Final Outcomes: Decoding Relative Long-Horizon Candidate Value from Reasoning Model Representations The paper constructs a dataset to show that relative candidate token value in multi-step reasoning is linearly decodable from model representations. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Does not study retrieval-augmented generation or external document grounding. | |
| From Candidate Tokens to Final Outcomes: Decoding Relative Long-Horizon Candidate Value from Reasoning Model Representations The paper constructs a dataset to show that relative candidate token value in multi-step reasoning is linearly decodable from model representations. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Analyzes text reasoning hidden states without multimodal capabilities. | |
| AlphaPilot: Multi-Turn Tool-Using LLM Agents for Adaptive Alpha Mining with Reinforcement Learning The paper introduces a reinforcement learning framework that trains tool-using agents to generate and refine financial alpha factor formulas. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Deals with financial text and market data, not multimodal VLMs. | |
| AlphaPilot: Multi-Turn Tool-Using LLM Agents for Adaptive Alpha Mining with Reinforcement Learning The paper introduces a reinforcement learning framework that trains tool-using agents to generate and refine financial alpha factor formulas. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Focuses on financial return optimization rather than safety guardrails. | |
| AlphaPilot: Multi-Turn Tool-Using LLM Agents for Adaptive Alpha Mining with Reinforcement Learning The paper introduces a reinforcement learning framework that trains tool-using agents to generate and refine financial alpha factor formulas. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Focuses on agent tool queries rather than RAG architectures. | |
| AlphaPilot: Multi-Turn Tool-Using LLM Agents for Adaptive Alpha Mining with Reinforcement Learning The paper introduces a reinforcement learning framework that trains tool-using agents to generate and refine financial alpha factor formulas. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Does not address model compression, pruning, or serving efficiency. | |
| AlphaPilot: Multi-Turn Tool-Using LLM Agents for Adaptive Alpha Mining with Reinforcement Learning The paper introduces a reinforcement learning framework that trains tool-using agents to generate and refine financial alpha factor formulas. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Proposes an alpha-mining agent rather than an LLM benchmark. | |
| AlphaPilot: Multi-Turn Tool-Using LLM Agents for Adaptive Alpha Mining with Reinforcement Learning The paper introduces a reinforcement learning framework that trains tool-using agents to generate and refine financial alpha factor formulas. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Generates mathematical alpha formula expressions, not software engineering. | |
| Concept Representations in Vector Space: Quantifying Sharedness and Idiosyncrasy of Meaning The paper develops a measurement framework using sentence embeddings to quantify shared versus individual-specific variation in concept definitions. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Studies semantic concept variance, not retrieval-augmented generation systems. | |
| Concept Representations in Vector Space: Quantifying Sharedness and Idiosyncrasy of Meaning The paper develops a measurement framework using sentence embeddings to quantify shared versus individual-specific variation in concept definitions. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Analyzes sentence embeddings of definitions, not autonomous agents. | |
| Concept Representations in Vector Space: Quantifying Sharedness and Idiosyncrasy of Meaning The paper develops a measurement framework using sentence embeddings to quantify shared versus individual-specific variation in concept definitions. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Investigates text embeddings without multimodal vision or audio inputs. | |
| Concept Representations in Vector Space: Quantifying Sharedness and Idiosyncrasy of Meaning The paper develops a measurement framework using sentence embeddings to quantify shared versus individual-specific variation in concept definitions. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Studies cognitive semantics, not code generation or repair. | |
| Concept Representations in Vector Space: Quantifying Sharedness and Idiosyncrasy of Meaning The paper develops a measurement framework using sentence embeddings to quantify shared versus individual-specific variation in concept definitions. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Does not cover model compression, distillation, or serving efficiency. | |
| Concept Representations in Vector Space: Quantifying Sharedness and Idiosyncrasy of Meaning The paper develops a measurement framework using sentence embeddings to quantify shared versus individual-specific variation in concept definitions. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Focuses on psycholinguistic measurement rather than LLM benchmarking. | |
| Concept Representations in Vector Space: Quantifying Sharedness and Idiosyncrasy of Meaning The paper develops a measurement framework using sentence embeddings to quantify shared versus individual-specific variation in concept definitions. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Analyzes human concept representation rather than deployment safety risks. | |
| A Step in the Right Place: Understanding and Optimizing Timestep Placement in Continuous Diffusion Language Models The paper introduces an algorithm to optimize denoising timestep placement in continuous diffusion language models to improve generation quality. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Addresses diffusion sampling efficiency rather than safety or reliability. | |
| A Step in the Right Place: Understanding and Optimizing Timestep Placement in Continuous Diffusion Language Models The paper introduces an algorithm to optimize denoising timestep placement in continuous diffusion language models to improve generation quality. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Focuses on continuous diffusion decoders, not agent planning. | |
| A Step in the Right Place: Understanding and Optimizing Timestep Placement in Continuous Diffusion Language Models The paper introduces an algorithm to optimize denoising timestep placement in continuous diffusion language models to improve generation quality. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Investigates continuous text generation, not code synthesis. | |
| A Step in the Right Place: Understanding and Optimizing Timestep Placement in Continuous Diffusion Language Models The paper introduces an algorithm to optimize denoising timestep placement in continuous diffusion language models to improve generation quality. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Focuses on sampling algorithms rather than evaluation benchmarks. | |
| A Step in the Right Place: Understanding and Optimizing Timestep Placement in Continuous Diffusion Language Models The paper introduces an algorithm to optimize denoising timestep placement in continuous diffusion language models to improve generation quality. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Does not address external knowledge retrieval or RAG pipelines. | |
| A Step in the Right Place: Understanding and Optimizing Timestep Placement in Continuous Diffusion Language Models The paper introduces an algorithm to optimize denoising timestep placement in continuous diffusion language models to improve generation quality. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Focuses on diffusion for text generation without multimodal inputs. | |
| Apertus 1.5: From Open Text Models to Open Multimodal Reasoners The paper presents open-source multimodal models extended to process text, images, and audio with long-context reasoning capabilities. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Presents an open multimodal model rather than deployment safety. | |
| Apertus 1.5: From Open Text Models to Open Multimodal Reasoners The paper presents open-source multimodal models extended to process text, images, and audio with long-context reasoning capabilities. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Does not focus on retrieval-augmented generation architectures. | |
| Apertus 1.5: From Open Text Models to Open Multimodal Reasoners The paper presents open-source multimodal models extended to process text, images, and audio with long-context reasoning capabilities. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Describes a base foundation model release, not an agent. | |
| Apertus 1.5: From Open Text Models to Open Multimodal Reasoners The paper presents open-source multimodal models extended to process text, images, and audio with long-context reasoning capabilities. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Evaluates a new model release rather than benchmarking methodology. | |
| Apertus 1.5: From Open Text Models to Open Multimodal Reasoners The paper presents open-source multimodal models extended to process text, images, and audio with long-context reasoning capabilities. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Focuses on 8B/70B pretraining rather than efficient model compression. | |
| Apertus 1.5: From Open Text Models to Open Multimodal Reasoners The paper presents open-source multimodal models extended to process text, images, and audio with long-context reasoning capabilities. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant A general multimodal foundation model, not specifically for code. | |
| An Interpretable Linguistically-Grounded Analysis of Scene Saliency in Movie Screenplays The paper evaluates whether uniform information density and specific linguistic features predict salient scenes in movie screenplays. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Explores narrative linguistic features rather than AI safety or reliability. | |
| An Interpretable Linguistically-Grounded Analysis of Scene Saliency in Movie Screenplays The paper evaluates whether uniform information density and specific linguistic features predict salient scenes in movie screenplays. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Analyzes screenplay saliency using NLP, not autonomous agent systems. | |
| An Interpretable Linguistically-Grounded Analysis of Scene Saliency in Movie Screenplays The paper evaluates whether uniform information density and specific linguistic features predict salient scenes in movie screenplays. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Does not address model compression, pruning, or efficient inference. | |
| An Interpretable Linguistically-Grounded Analysis of Scene Saliency in Movie Screenplays The paper evaluates whether uniform information density and specific linguistic features predict salient scenes in movie screenplays. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Studies linguistic salience rather than LLM benchmarking methodologies. | |
| An Interpretable Linguistically-Grounded Analysis of Scene Saliency in Movie Screenplays The paper evaluates whether uniform information density and specific linguistic features predict salient scenes in movie screenplays. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Does not explore document retrieval or RAG architectures. | |
| An Interpretable Linguistically-Grounded Analysis of Scene Saliency in Movie Screenplays The paper evaluates whether uniform information density and specific linguistic features predict salient scenes in movie screenplays. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Studies movie screenplays rather than software programming code. | |
| An Interpretable Linguistically-Grounded Analysis of Scene Saliency in Movie Screenplays The paper evaluates whether uniform information density and specific linguistic features predict salient scenes in movie screenplays. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Focuses purely on text screenplay analysis without multimodal processing. | |
| The Fragility and Dynamics of LLM Pruning-Recovery Steps The paper analyzes how pruning and subsequent recovery stages interact, finding instruction-following collapses earlier than domain knowledge during model compression. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Focuses on general LLM compression rather than code generation. | |
| The Fragility and Dynamics of LLM Pruning-Recovery Steps The paper analyzes how pruning and subsequent recovery stages interact, finding instruction-following collapses earlier than domain knowledge during model compression. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Evaluates pruning recipes rather than creating evaluation benchmarks. | |
| The Fragility and Dynamics of LLM Pruning-Recovery Steps The paper analyzes how pruning and subsequent recovery stages interact, finding instruction-following collapses earlier than domain knowledge during model compression. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Addresses standard text LLM compression without multimodal data. | |
| The Fragility and Dynamics of LLM Pruning-Recovery Steps The paper analyzes how pruning and subsequent recovery stages interact, finding instruction-following collapses earlier than domain knowledge during model compression. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Investigates structural pruning dynamics, not autonomous agentic workflows. | |
| The Fragility and Dynamics of LLM Pruning-Recovery Steps The paper analyzes how pruning and subsequent recovery stages interact, finding instruction-following collapses earlier than domain knowledge during model compression. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Studies model compression dynamics rather than deployment safety guardrails. | |
| The Fragility and Dynamics of LLM Pruning-Recovery Steps The paper analyzes how pruning and subsequent recovery stages interact, finding instruction-following collapses earlier than domain knowledge during model compression. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Does not investigate retrieval systems or knowledge grounding. | |
| Towards Robust Calibration Dataset for Domain-General Mixture of Experts Pruning The paper studies how calibration dataset size, language, and data distribution affect post-training expert pruning in mixture-of-experts models. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Investigates compression calibration rather than safety or reliability guardrails. | |
| Towards Robust Calibration Dataset for Domain-General Mixture of Experts Pruning The paper studies how calibration dataset size, language, and data distribution affect post-training expert pruning in mixture-of-experts models. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Focuses on general MoE compression, not software engineering. | |
| Towards Robust Calibration Dataset for Domain-General Mixture of Experts Pruning The paper studies how calibration dataset size, language, and data distribution affect post-training expert pruning in mixture-of-experts models. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Does not study document retrieval or augmented generation. | |
| Towards Robust Calibration Dataset for Domain-General Mixture of Experts Pruning The paper studies how calibration dataset size, language, and data distribution affect post-training expert pruning in mixture-of-experts models. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Focuses on expert pruning, not LLM agent systems. | |
| Towards Robust Calibration Dataset for Domain-General Mixture of Experts Pruning The paper studies how calibration dataset size, language, and data distribution affect post-training expert pruning in mixture-of-experts models. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Studies pruning calibration data rather than evaluation benchmark design. | |
| Towards Robust Calibration Dataset for Domain-General Mixture of Experts Pruning The paper studies how calibration dataset size, language, and data distribution affect post-training expert pruning in mixture-of-experts models. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Evaluates language models without multimodal text-image-audio inputs. | |
| The Gradient That Top-k Distillation Never Computes The paper analyzes gradient dynamics in top-k distillation, showing that renormalization prevents optimization along directions controlling tail token probabilities. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Deals with general vocabulary distillation, not code-specific models. | |
| The Gradient That Top-k Distillation Never Computes The paper analyzes gradient dynamics in top-k distillation, showing that renormalization prevents optimization along directions controlling tail token probabilities. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Focuses on distillation training loss rather than deployment safety. | |
| The Gradient That Top-k Distillation Never Computes The paper analyzes gradient dynamics in top-k distillation, showing that renormalization prevents optimization along directions controlling tail token probabilities. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Analyzes training distillation gradients rather than evaluation benchmarks. | |
| The Gradient That Top-k Distillation Never Computes The paper analyzes gradient dynamics in top-k distillation, showing that renormalization prevents optimization along directions controlling tail token probabilities. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Studies language model token vocabulary distillation without multimodality. | |
| The Gradient That Top-k Distillation Never Computes The paper analyzes gradient dynamics in top-k distillation, showing that renormalization prevents optimization along directions controlling tail token probabilities. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Does not address external document retrieval or knowledge grounding. | |
| The Gradient That Top-k Distillation Never Computes The paper analyzes gradient dynamics in top-k distillation, showing that renormalization prevents optimization along directions controlling tail token probabilities. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Investigates theoretical distillation loss gradients, not agentic planning systems. | |
| Feedback as an Interface: Controlling Form and Detail in LLM Translation Refinement The paper investigates how varying the detail and format of feedback affects machine translation refinement in large language models. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Studies iterative machine translation refinement, not autonomous multi-step agents. | |
| Feedback as an Interface: Controlling Form and Detail in LLM Translation Refinement The paper investigates how varying the detail and format of feedback affects machine translation refinement in large language models. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Deals with natural language translation, not code or programming. | |
| Feedback as an Interface: Controlling Form and Detail in LLM Translation Refinement The paper investigates how varying the detail and format of feedback affects machine translation refinement in large language models. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Focuses entirely on text translation without multimodal inputs. | |
| Feedback as an Interface: Controlling Form and Detail in LLM Translation Refinement The paper investigates how varying the detail and format of feedback affects machine translation refinement in large language models. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Does not evaluate external document retrieval or grounding methods. | |
| Feedback as an Interface: Controlling Form and Detail in LLM Translation Refinement The paper investigates how varying the detail and format of feedback affects machine translation refinement in large language models. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Does not explore quantization, pruning, or efficient model serving. |
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