What it shows
CLaRa enhances retrieval-augmented generation by introducing unified embedding-based compression and joint optimization, achieving state-of-the-art performance in QA benchmarks.
By Jie He, Richard He Bai, Sinead Williamson and 3 more · arXiv 2511.18659 · PDF · Code
UnverifiedHugging Face's summary; not yet checked by hand.
More from Apple
All 11OpenVLHarness: Real World Visual Agentic Tool-Use with Multimodal MemoryThis framework improves multimodal AI reasoning by providing persistent memory and tool interfaces to help models process and combine complex visual evidence.
Apple101 upvotesRead →Decoding Looped Transformers Better for (Almost) FreeLoopCD improves decoding quality in looped transformers by contrasting final predictions with earlier recurrent states without extra training.
Apple1 citations46 upvotesRead →MintAct: A Unified Visual Agent for Digital EnvironmentsMintAct is a unified vision-language model family that handles UI grounding and multi-step navigation across various digital environments.
Apple35 upvotesRead →It Takes Two to Match: Co-Evolving Generative Retriever with Reinforcement LearningCoGR trains LLMs to generate compact keywords for both queries and items, enabling direct inverted-index retrieval optimized via co-evolving reinforcement learning.
Apple2 citations65 upvotesRead →Embarrassingly Simple Self-Distillation Improves Code GenerationSimple self-distillation improves code generation in large language models by fine-tuning on model-generated samples, effectively addressing precision-exploration trade-offs in decoding.
Apple40 citations55 upvotesRead →CHIMERA: Compact Synthetic Data for Generalizable LLM ReasoningA synthetic reasoning dataset called CHIMERA is introduced to overcome data-centric challenges in training large language models for cross-domain reasoning, achieving performance comparable to much larger models.
Apple2 citations57 upvotesRead →Sharp Monocular View Synthesis in Less Than a SecondSHARP synthesizes photorealistic views from a single image using a 3D Gaussian representation, achieving state-of-the-art results with rapid processing.
Apple32 citations31 upvotesRead →One Layer Is Enough: Adapting Pretrained Visual Encoders for Image GenerationFAE, a framework using a feature auto-encoder and dual decoders, adapts pre-trained visual representations for generative models, achieving high performance in image generation tasks.
Apple32 citations25 upvotesRead →
Other reasoning papers
TopicChain-of-Thought Prompting Elicits Reasoning in Large Language ModelsAsking a model to write out its intermediate steps (chain of thought) sharply improves its math and logic answers.
Google23.8k citations16 upvotesRead →DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language ModelsDeepSeekMath 7B improves mathematical reasoning through enhanced data pre-training and Group Relative Policy Optimization, achieving high scores on MATH benchmark without external tools.
DeepSeek10.6k citations152 upvotesRead →DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement LearningReinforcement learning taught a model long step-by-step reasoning on par with OpenAI o1.
DeepSeek5,972 citations458 upvotesRead →DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code IntelligenceDeepSeek-Coder-V2, a Mixture-of-Experts language model, excels in code-specific tasks by enhancing coding and mathematical reasoning capabilities while expanding language support and context length.
DeepSeek530 citations73 upvotesRead →MolmoAct: Action Reasoning Models that can Reason in SpaceAction Reasoning Models (ARMs) integrate perception, planning, and control to enable adaptable and explainable robotic behavior, achieving superior performance across various tasks and settings.
Ai2232 citations45 upvotesRead →ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent PlanningThinkAct, a dual-system framework, uses reinforced visual latent planning to enable few-shot adaptation, long-horizon planning, and self-correction in embodied AI tasks by bridging high-level reasoning with low-level action execution.
NVIDIA192 citations43 upvotesRead →
About this paper
- Venue
- arXiv.org
- Citations
- 17, 0 influential · Semantic Scholar
- Upvotes
- 25 · Hugging Face
- Lab
- Apple · on Companies · on Quarterly · on Paydays · on TechConf
Changes
Citations rose from 13 to 17.
Citations rose from 12 to 13.
Citations rose from 11 to 12.
A first count of 0 influential citations.
A first count of 11 citations.
Found by the weekly Hugging Face scan; not yet checked by hand.