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Channel Posts
Memanto: Typed Semantic Memory with Information-Theoretic Retrieval for Long-Horizon Agents
📅 Publication Date: Apr 23, 2026
📑 Paper: https://arxiv.org/pdf/2604.22085
💻 Project Page: https://memanto.ai/
🔗 Code: https://github.com/moorcheh-ai/memanto-evaluation
🗃 Datasets citing this paper:
• https://huggingface.co/datasets/moorcheh/memanto-longmem-results
• https://huggingface.co/datasets/moorcheh/memanto-locomo-results
📝 Description:
Memanto introduces a universal, typed semantic memory layer for AI agents that bypasses complex semantic graphs. It uses an information-theoretic search engine for fast, overhead-free retrieval. This system achieves state-of-the-art accuracy on benchmarks with a single query and no ingestion cost.
#AI #SemanticMemory #InformationRetrieval #AIAgents #MachineLearning
| 2 |  Probing Visual Planning in Image Editing Models
📅 Publication Date: Apr 23, 2026
📑 Paper: https://arxiv.org/pdf/2604.22868
💻 Project Page: https://spatigen.github.io/amaze.io/
🔗 Github: https://github.com/spatigen/amaze
📝 Description:
This paper redefines visual planning as a single-step image transformation using abstract puzzles for evaluation. Their EAR paradigm and AMAZE dataset reveal that current AI models, despite finetuning, cannot match human zero-shot efficiency, highlighting a gap in visual reasoning.
#VisualPlanning #ImageEditing #ComputerVision #AIResearch #MachineLearning | 71 |
| 3 | Lip Forcing: Few-Step Autoregressive Diffusion for Real-time Lip Synchronization
📅 Publication Date: Jun 9, 2026
📑 Paper: https://arxiv.org/pdf/2606.11180
💻 Project Page: https://cvlab-kaist.github.io/LipForcing/
📝 Description:
The paper presents Lip Forcing, a novel autoregressive diffusion method for real-time video-to-video lip synchronization. The problem with existing diffusion-based lip synchronization models is that they require full-sequence bidirectional attention and many denoising steps, making them impractical for real-time inference. To address this issue, the authors propose a method that distills a large audio-conditioned bidirectional video diffusion teacher into smaller causal students.
#LipSynchronization #AutoregressiveDiffusion #RealTimeVideoProcessing #VideoToVideoSynthesis #DiffusionBasedModels | 114 |
| 4 |  Contexts are Never Long Enough: Structured Reasoning for Scalable Question Answering over Long Document Sets
📅 Publication Date: Apr 24, 2026
📑 Paper: https://arxiv.org/pdf/2604.22294
🔗 Code: N/A
📝 Description:
SLIDERS tackles long-document QA by extracting information into a relational database and using SQL for structured reasoning. This avoids LLM context window issues and aggregation bottlenecks, significantly outperforming traditional methods on various benchmarks.
#QuestionAnswering #NLP #AI #SQL #LongDocuments | 126 |
| 5 | LLM Safety From Within: Detecting Harmful Content with Internal Representations
📅 Publication Date: Apr 20, 2026
📑 Paper: https://arxiv.org/pdf/2604.18519
🔗 Code: https://github.com/CSSLab/SIREN
📊 Models citing this paper:
• https://huggingface.co/UofTCSSLab/SIREN-Qwen3-0.6B
• https://huggingface.co/UofTCSSLab/SIREN-Qwen3-4B
• https://huggingface.co/UofTCSSLab/SIREN-Llama-3.2-1B
📝 Description:
SIREN is a lightweight guard model that uses LLM internal layer features to detect harmful content, outperforming current models. It is more efficient, generalizes better, and requires significantly fewer parameters than existing guard models.
#LLMSafety #AIethics #HarmfulContent #DeepLearning #NLP | 175 |
| 6 |  TexOCR: Advancing Document OCR Models for Compilable Page-to-LaTeX Reconstruction
📅 Publication Date: Apr 24, 2026
📑 Paper: https://arxiv.org/pdf/2604.22880
🔗 Github: https://github.com/QDRhhhh/TexOCR
📝 Description:
This research presents TexOCR for reconstructing scientific PDFs into compilable LaTeX, addressing limitations of current OCR. It introduces a new benchmark and trains TexOCR using reinforcement learning with verifiable rewards.
#AI #DataScience #MachineLearning #Research | 224 |
| 7 | Type-Checked Compliance: Deterministic Guardrails for Agentic Financial Systems Using Lean 4 Theorem Proving
📅 Publication Date: Apr 1, 2026
📑 Paper: https://arxiv.org/pdf/2604.01483
💻 Project Page: https://axiom.devrashie.space
🔗 Code: https://github.com/arkanemystic/lean-agent-protocol
📝 Description:
The Lean-Agent Protocol ensures deterministic regulatory compliance for financial AI. It uses Lean 4 theorem proving to auto-formalize policies, verifying agent actions as mathematical conjectures for cryptographic-level certainty, addressing LLM probabilistic nature.
#FormalVerification #AICompliance #FinTech #Lean4 #LLMAgents | 250 |
| 8 | Do Audio-Visual Large Language Models Really See and Hear?
📅 Publication Date: Apr 3, 2026
📑 Paper: https://arxiv.org/pdf/2604.02605
💻 Project Page: https://ramaneswaran.github.io/avllm_interpretability/
🔗 Code: https://github.com/ramaneswaran/avllm_interpretability
📝 Description:
AVLLMs exhibit modality bias where visual representations dominate over audio cues during multimodal integration, despite audio semantics being present in intermediate layers.
#AI #DataScience #MachineLearning #HuggingFace #Research | 279 |
| 9 |  Scaling Teams or Scaling Time? Memory Enabled Lifelong Learning in LLM Multi-Agent Systems
📅 Publication Date: Mar 27, 2026
📑 Paper: https://arxiv.org/pdf/2604.03295
🔗 Code: https://github.com/ShanglinWu/MAS_lifelong_learning
📝 Description:
This paper introduces LLMA-Mem, a memory framework for LLM multi-agent systems. It finds that scaling is non-monotonic; optimized experience reuse allows smaller teams to outperform larger ones, improving long-term performance and reducing cost.
#AI #DataScience #MachineLearning #Research | 277 |
| 10 | BidirLM: From Text to Omnimodal Bidirectional Encoders by Adapting and Composing Causal LLMs
📅 Publication Date: Apr 2, 2026
📑 Paper: https://arxiv.org/pdf/2604.02045
🔗 Code: N/A
📊 Models citing this paper:
• https://huggingface.co/BidirLM/BidirLM-Omni-2.5B-Embedding
• https://huggingface.co/BidirLM/BidirLM-0.6B-Embedding
• https://huggingface.co/BidirLM/BidirLM-1.7B-Embedding
🗃 Datasets citing this paper:
• https://huggingface.co/datasets/BidirLM/BidirLM-Contrastive
📝Description:
BidirLM adapts causal LLMs into bidirectional encoders, overcoming catastrophic forgetting and integrating specialized models. It employs a prior masking phase, weight merging, and data mixture, outperforming alternatives on text, vision, and audio benchmarks.
#LLM #MultimodalAI #DeepLearning #AIResearch #HuggingFace #ModelAdaptation | 313 |
| 11 | Claw-Eval: Toward Trustworthy Evaluation of Autonomous Agents
📅 Publication Date: Apr 7, 2026
📑 Paper: https://arxiv.org/pdf/2604.06132
💻 Project Page: https://claw-eval.github.io/
🔗 Code: https://github.com/claw-eval/claw-eval
📝 Description:
Claw-Eval addresses limitations in agent benchmarks by providing comprehensive evaluation across multiple modalities with trajectory-aware grading and safety assessments.
#AI #DataScience #MachineLearning #Research | 300 |
| 12 | Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework
📅 Publication Date: Apr 7, 2026
📑 Paper: https://arxiv.org/pdf/2604.06170
💻 Project Page: https://papercircle.vercel.app/
🔗 Code: https://github.com/MAXNORM8650/papercircle
📝Description:
A multi-agent system called Paper Circle is presented that automates the discovery and analysis of scientific literature through integrated retrieval and knowledge graph construction capabilities.
#AI #DataScience #MachineLearning #Research | 291 |
| 13 |  MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU
📅 Publication Date: Apr 6, 2026
📑 Paper: https://arxiv.org/pdf/2604.05091
🔗 Code: https://github.com/DLYuanGod/MegaTrain
📝 Description:
MegaTrain trains large language models with over 100 billion parameters on a single GPU. It stores parameters in host memory and streams them to the GPU using pipelined execution and stateless layer templates to overcome bandwidth. This enables 120 billion parameter training.
#AI #DataScience #MachineLearning #Research | 314 |
| 14 |  Context-Value-Action Architecture for Value-Driven Large Language Model Agents
📅 Publication Date: Apr 7, 2026
📑 Paper: https://arxiv.org/pdf/2604.05939
📝 Description:
LLMs show rigid, polarized behavior worsening with reasoning. The Context-Value-Action CVA architecture decouples actions from reasoning using a human-data Value Verifier, mitigating polarization and improving behavioral fidelity.
#AI #DataScience #MachineLearning #Research | 310 |
| 15 | QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization
📅 Publication Date: Apr 7, 2026
📑 Paper: https://arxiv.org/pdf/2604.05963
🔗 Code: https://github.com/kcxain/QiMeng-PRepair
📊 Models citing this paper:
• https://huggingface.co/kcxain/Prepair-Python-7B-EA
• https://huggingface.co/kcxain/Prepair-Verilog-7B-EA
📝 Description:
PRepair tackles over-editing in AI program repair by maximizing correct code reuse. It combines controlled bug injection and edit-aware policy optimization using an edit-aware reward. This framework significantly improves repair precision and decoding throughput.
#ProgramRepair #AI #MachineLearning #ReinforcementLearning #SoftwareEngineering | 345 |
| 16 |  Think in Strokes, Not Pixels: Process-Driven Image Generation via Interleaved Reasoning
📅 Publication Date: Apr 8, 2026
📑Paper: https://arxiv.org/pdf/2604.04746
🔗 Code: N/A
📝 Description:
This paper introduces process-driven image generation, an iterative method with interleaved textual and visual reasoning. It decomposes synthesis into planning, drafting, reflecting, and refining steps. Dense step-wise supervision ensures consistency and interpretability of intermediate states.
#ImageGeneration #GenerativeAI #ArtificialIntelligence #DeepLearning #ComputerVision | 339 |
| 17 | Watch Before You Answer: Learning from Visually Grounded Post-Training
📅 Publication Date: Apr 6, 2026
📑 Paper: https://arxiv.org/pdf/2604.05117
💻 Project Page: http://vidground.etuagi.com
🔗 Code: https://github.com/reacher-z/vidground
📝 Description:
VLMs struggle with video understanding due to text biases in benchmarks and training data. VidGround uses only visually grounded questions for post-training to eliminate these biases. This improves VLM performance and emphasizes the need for high-quality, visually grounded data.
#VLMs #VideoUnderstanding #AI #MachineLearning #ComputerVision | 360 |
| 18 |  Learning to Hint for Reinforcement Learning
📅 Publication Date: Apr 1, 2026
📑 Paper: https://arxiv.org/pdf/2604.00698
🔗 Code: https://github.com/Andree-9/HiLL
📝 Description:
HiLL is a reinforcement learning framework that adaptively generates hints conditioned on reasoner errors to improve learning signals and transfer performance in group relative policy optimization.
#AI #DataScience #MachineLearning #Research | 392 |
| 19 |  Online Experiential Learning for Language Models
📅 Publication Date: Mar 17, 2026
📑 Paper: https://arxiv.org/pdf/2603.16856
💻 Project Page: https://github.com/microsoft/LMOps/tree/main/oel
🔗 Code: https://github.com/microsoft/LMOps/tree/main/oel
📝 Description:
Online Experiential Learning enables continuous improvement of language models through deployment experience by extracting and consolidating experiential knowledge via on-policy distillation.
#AI #DataScience #MachineLearning #Research | 404 |
| 20 | Thinking in Uncertainty: Mitigating Hallucinations in MLRMs with Latent Entropy-Aware Decoding
📅 Publication Date: Mar 9, 2026
📑 Paper: https://arxiv.org/pdf/2603.13366
💻 Project Page: https://mlrm-lead.github.io/
🔗 Code: https://github.com/mlrm-LEAD/mlrm-LEAD
#AI #DataScience #MachineLearning #Research | 408 |
