Papersby GlyphSelection-Based Structured Reasoning: Toward Efficient Multimodal Search Agents research paper by Carnegie Mellon University, 2026
Carnegie Mellon University · Reasoning · Oct 1, 2026 · 0 citations · 37 upvotes · unverified
What it shows
SSR improves multimodal agent efficiency by replacing free-form reasoning generation with a selection process from pre-specified reasoning candidates.
By Feiyu Gavin Zhu, Xiaoyu Zhu, Jiqi Yang and 9 more · arXiv 2610.01892 · PDF · Code
UnverifiedSummarised by a model from the abstract; not yet checked by hand.
More from Carnegie Mellon University
All 11Random Is Almost Enough? Stable and Low-Cost Benchmark Compression via BestRandBestRand is a method for selecting small benchmark subsets that reliably estimate full evaluation scores at lower computational costs.
Carnegie Mellon University51 upvotesRead →Eat Your Precision: Precision Objective Mitigates Reward Hacking in Rubric-based RLFORK jointly optimizes recall and precision via rubric-driven necessity credits and factual grading to improve response quality and reduce verbosity in reinforcement learning.Carnegie Mellon University34 upvotesRead →Into the Unknown: Benchmarking AI agents on open scientific research problemsThis benchmark evaluates how well AI agents can write follow-up scientific research papers based on unpublished directions from domain experts.Carnegie Mellon University62 upvotesRead →X-Ragent: Tool-Augmented Reinforcement Learning for Evidence-Grounded Chest X-Ray ReasoningThis paper introduces a reinforcement learning framework for medical agents that selectively uses external evidence to improve diagnostic reasoning in chest X-rays.Carnegie Mellon University44 upvotesRead →Predictive Credit: Measuring What Scientific Explanations Add to Experimental ForecastsThis paper introduces a protocol to measure how much scientific explanations actually improve the accuracy of experimental forecasts.Carnegie Mellon University103 upvotesRead →Training Object Permanence in World ModelsThis paper introduces a dataset and training stack to improve object permanence and physical reasoning in video world models.Carnegie Mellon University242 upvotesRead →JEV-as-a-Judge: Accept When Confident, Escalate When UnsureJEV-as-a-Judge is a cost-effective evaluation framework that uses a simple decision-only judge for easy cases and escalates complex ones.Carnegie Mellon University57 citations42 upvotesRead →Benchmark Radar: A Living Database and Search Engine for AI Benchmarks and EvaluationBenchmark Radar is a searchable living database and discovery engine for AI evaluation benchmarks that aggregates sources, score histories, and evidence to support benchmark selection and comparison.Carnegie Mellon University171 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
- Citations
- 0, 0 influential · Semantic Scholar
- Upvotes
- 37 · Hugging Face
- Lab
- Carnegie Mellon University
Changes
A first count of 0 influential citations.
A first count of 0 citations.
SSR improves multimodal agent efficiency by replacing free-form reasoning generation with a selection process from pre-specified reasoning candidates.
Found by the weekly Hugging Face scan; not yet checked by hand.