Natural Language Processing

ResearchBench: Benchmarking LLMs for Scientific Discovery via Inspiration-Based Task Decomposition.

ReFeed enhances multi-dimensional summarization by using reflective reasoning on feedback, mitigating trade-offs between dimensions and improving robustness.

ReaRAG enhances factuality in large reasoning models (LRMs) by integrating knowledge-guided reasoning with iterative retrieval augmented generation.

This survey presents a methodology-centered taxonomy of LLM agent systems, linking design principles to emergent behaviors and identifying future research directions.

OlymMATH: A new Olympiad-level math benchmark rigorously tests LLMs’ reasoning, revealing limitations and paving the way for advancements.

Survey on improving efficiency in large reasoning models across language, multimodality, and beyond.

Open Deep Search (ODS): Democratizing Search with Open-source Reasoning Agents.

MCTS-RAG: Combines Monte Carlo Tree Search with Retrieval-Augmented Generation to enhance small LMs’ reasoning on complex tasks.

Boost LLM reasoning by having models ‘Think Twice’! This novel method iteratively refines answers, significantly enhancing accuracy on complex tasks.

FFN Fusion: Parallelizing sequential computation in large language models for significant speedups!

LLMs falter on culturally adapted math problems, revealing a critical cultural bias.

V-SEEK accelerates LLM reasoning on open-hardware RISC-V platforms, achieving up to 3.0x speedup through optimized kernels and memory management.

This paper introduces deviation-factored post-training methods to enhance diversity and quality in creative LLM writing.

MARS: Optimizing prompts with multi-agent collaboration and Socratic learning for better LLM performance!

LEMMA: LLMs learn math via mistake analysis and correction, boosting performance without external critics.

XAttention: Antidiagonal scoring unlocks block-sparse attention, slashing compute costs in long-context Transformers without sacrificing accuracy.

Typed-RAG enhances non-factoid QA by type-aware decomposition, refining retrieval and generation for nuanced, user-aligned answers.

A comprehensive survey on evaluation methodologies for LLM-based agents, analyzing benchmarks and frameworks across key dimensions like capabilities, applications, and generalist performance.

LLMs survey: Model, output, and prompt-based strategies for efficient reasoning, mitigating ‘overthinking’ for faster, cheaper, and real-world applications.

MathFusion: Instruction Fusion enhances LLM’s math problem-solving!