Notable data and AI papers

627 papers · 45 labs

Planning In Natural Language Improves LLM Search For Code Generation research paper by Scale AI, 2024

Scale AI · Retrieval and data · Sep 5, 2024 · 101 citations

Read on arXiv

What it shows

PLANSEARCH, a novel search algorithm, improves performance in coding benchmarks by generating a diverse set of natural language plans, outperforming existing methods through increased diversity in solutions.

By Evan Wang, Federico Cassano, Catherine Wu and 7 more · arXiv 2409.03733 · PDF

Hugging Face's summary; not yet checked by hand.

More from Scale AI

All 25
Agents and evaluationSep 11, 2026Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent FailuresContinual Search improves automated root-cause attribution in long agent execution traces by iteratively prompting diagnosis until unresolved evidence is found.Scale AI5 upvotesRead →Multimodal and roboticsSep 11, 2026SteerDuplex: Steerable Duplex Speech Dialogue ModelsA full-duplex speech model that follows spoken instructions on tone, persona, pace and voice, with a benchmark of 390 prompts to test it.Scale AI3 citations6 upvotesRead →Agents and evaluationSep 9, 2026Studying Without a Syllabus: Task-Agnostic Environment PreprocessingAn agent can explore unfamiliar environments without task-specific guidance to build reusable artifacts that reduce later inference costs, though larger study budgets do not always improve results.Scale AI1 citations4 upvotesRead →Agents and evaluationAug 6, 2026HarnessOpt-Bench: Evaluating LLMs at Harness OptimizationHarnessOpt-Bench measures how well frontier LLMs iteratively improve agent harnesses under constrained evaluation budgets, revealing substantial variation across models and tasks.Scale AI12 citations35 upvotesRead →Agents and evaluationJul 30, 2026Model or Harness? An Interaction-Centric Taxonomy for Localizing Agent FailuresAn interaction-centric taxonomy localizes agent failures to specific component interactions to guide targeted repairs across diverse architectures.Scale AI11 citations10 upvotesRead →Agents and evaluationJun 29, 2026SWE-INTERACT: Reimagining SWE Benchmarks as User-Driven Long-Horizon Coding SessionsSWE-Interact presents a testbed that evaluates coding agents in realistic multi-turn, user-driven software engineering scenarios, revealing significant gaps between single-turn performance and interactive task completion.Scale AI23 citations7 upvotesRead →Training and scalingMay 19, 2026Not Every Rubric Teaches Equally: Policy-Aware Rubric Rewards for RLVRPOW3R is a policy-aware framework for reinforcement learning with rubric-based rewards that adapts criterion weights during training to improve policy optimization while preserving human-defined criteria importance.Scale AI13 citations5 upvotesRead →ReasoningMay 12, 2026Reward Hacking in Rubric-Based Reinforcement LearningResearch examines reward hacking in rubric-based reinforcement learning, identifying verifier failure and rubric-design limitations as key sources of divergence between training and evaluation metrics.Scale AI33 citations4 upvotesRead →
Topic
Retrieval and dataUnverifiedNov 9, 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language ModelBLOOM, a 176B-parameter open-access language model, achieves competitive performance on various benchmarks and is released publicly with a Responsible AI License.Hugging Face3,165 citations41 upvotesRead →Retrieval and dataApr 24, 2024From Local to Global: A Graph RAG Approach to Query-Focused SummarizationGraphRAG builds a knowledge graph of a document set so a model can answer questions about the whole collection.Microsoft2,561 citations7 upvotesRead →Retrieval and dataUnverifiedAug 13, 2025DINOv3DINOv3, a self-supervised learning model, achieves superior performance across various vision tasks by scaling datasets and models, addressing dense feature degradation, and enhancing flexibility with post-hoc strategies.Meta1,932 citations320 upvotesRead →Retrieval and dataUnverifiedJan 8, 2026Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and RankingThe Qwen3-VL-Embedding and Qwen3-VL-Reranker models form an end-to-end multimodal search pipeline, leveraging multi-stage training and cross-attention mechanisms to achieve high-precision retrieval across diverse modalities.Alibaba (Qwen)315 citations59 upvotesRead →Retrieval and dataUnverifiedMay 23, 2024DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic DataGenerating extensive Lean 4 proof data from mathematical competition problems improved DeepSeekMath 7B's theorem-proving capabilities over GPT-4 and other methods.DeepSeek284 citations47 upvotesRead →Retrieval and dataFeb 9, 2024Aya Dataset: An Open-Access Collection for Multilingual Instruction TuningThe initiative builds a human-curated instruction-following dataset spanning 65 languages and creates the largest multilingual collection of instruction-following instances through templating and translating existing datasets across 114 languages, contributing datasets and platforms for participatory research.Cohere253 citations57 upvotesRead →
About this paper
Venue
arXiv.org
Citations
101, 6 influential · Semantic Scholar
Upvotes
0 · Hugging Face
Lab
Scale AI · on Companies · on Acquisitions · on awards · on breaches · on Attested · on Rounds · on Downtime · on labs · on Leaders · on marketplaces · on Allied · on Paydays · on quotes · on salaries · on Nines · on socials · on startups · on terms · on Titlewave

Changes

CitationsNov 2, 2026Citations rose from 98 to 101.
CitationsOct 12, 2026Citations rose from 95 to 98.
CitationsOct 5, 2026Citations rose from 93 to 95.
CitationsSep 28, 2026A first count of 6 influential citations.
CitationsSep 28, 2026A first count of 93 citations.
New paperSep 26, 2026Added to the list.