arXiv AI
Jul 2
Seed2.0 Model Card: Towards Intelligence Frontier for Real-World Complexity
arXiv:2607. 00248v1 Announce Type: new Abstract: We present Seed2.
Seed2.0 Model Card: Towards Intelligence Frontier for Real-World Complexity
We present Seed2. 0, a model series that takes a meaningful step toward solving complex, real-world tasks.
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arXiv Computation and Language
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InternBootcamp: Boosting LLM Reasoning with Verifiable Task Scaling
InternBootcamp is an open‑source framework that offers over 1,000 domain‑diverse task environments for large language model (LLM) reasoning research. It introduces Bootcamp‑Eval, an automatically generated benchmark for comprehensive performance assessment. Experiments show that training on InternBootcamp significantly improves reasoning performance, with a 32B model achieving state‑of‑the‑art results on Bootcamp‑Eval and other established benchmarks, demonstrating that scaling the number of training tasks yields consistent gains.
arXiv Machine Learning
Jun 15
Running the Gauntlet: Re-evaluating the Capabilities of Agents Beyond Familiar Environments
arXiv:2606. 14397v1 Announce Type: new Abstract: As agentic systems continue to evolve and are widely deployed in real-world scenarios, there is a growing demand to faithfully evaluate their capabilities.
arXiv AI
Sep 28
The Hard Part Comes After Search: Benchmarking Web Agents on Synthesizing, Organizing, and Displaying Knowledge
The paper introduces KNOWS, a benchmark for evaluating web agents that act as assistants by retrieving, synthesizing, and presenting information across complex, multi-step browser tasks. It outlines a task design rubric, evaluation protocol combining deterministic checks with LLM judgments, and reports that current agents achieve only modest success, with the best performing agent succeeding on less than 3% of tasks. The study highlights significant gaps in agents’ tool use, visual understanding, and long‑horizon reasoning.
arXiv Computer Vision
Sep 15
PuzzleMate: Benchmarking MLLMs for Egocentric Puzzle Assistance
arXiv:2609.14473v1 Announce Type: new Abstract: Personal AI assistants hold the potential to evolve from digital interfaces into embodied companions capable of guiding users through complex physical...
arXiv AI
Jul 24
Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents
arXiv:2602. 10226v2 Announce Type: replace-cross Abstract: Optimizing large-scale machine learning systems, such as recommendation models for global video platforms, requires navigating a massive hyperparameter search space and, more critically, designing sophisticated optimizers, architectures, and reward functions to capture nuanced user behaviors.
arXiv AI
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PuzzleJAX: A Benchmark for Reasoning and Learning
arXiv:2508.16821v2 Announce Type: replace Abstract: We introduce PuzzleJAX, a GPU-accelerated puzzle game engine and description language designed to support rapid benchmarking of tree search, reinfo...
arXiv AI
Sep 10
xDailyBench: Benchmarking LLMs on Professional Consultation for Real-Life Problems
xDailyBench is a new benchmark comprising 248 tasks across 51 real‑life scenarios, designed to evaluate large language models on everyday professional consultation. The tasks are based on actual user requests and assessed with detailed binary rubrics that capture both explicit instructions and implicit needs inferred from context. In tests of 11 leading models, the best achieved a 75.6% task‑level score, yet all models struggled more with implicit requirements, showing gaps of at least 9 percentage points.
arXiv AI
Oct 1
Generalising from Self-Produced Data: Model Training Beyond Human Constraints
arXiv:2504.04711v2 Announce Type: replace Abstract: Current large language models (LLMs) are constrained by human-derived training data and limited by a single level of abstraction that impedes defin...
arXiv Machine Learning
Sep 15
Running the Gauntlet: Challenging Agentic Tasks
arXiv:2606.14397v4 Announce Type: replace Abstract: As agentic systems continue to evolve and are widely deployed in real-world scenarios, there is a growing demand to faithfully evaluate their capab...
arXiv AI
Jun 30
HippoSpark: An On-Demand Experience System for LLM Reasoning
arXiv:2606. 29929v1 Announce Type: new Abstract: Distilling historical trajectories into reusable experience to enhance future problem-solving has become a focal point of recent LLM research.
arXiv AI
Jun 6
Benchmark Everything Everywhere All at Once
arXiv:2606. 06462v1 Announce Type: new Abstract: Benchmarks are fundamental for evaluating and advancing LLMs and MLLMs by providing standardized and explicit measures of performance.