Strong large language models (LLMs) often achieve high scores on many benchmarks, yet can fail on closely related, sometimes easier, problems near their apparent capability boundary, a phenomenon known as jagged intelligence that reveals reasoning weaknesses masked by aggregate scores. Existing evaluations of jagged intelligence rely on manually designed benchmark-adjacent rewrites, which are hard to scale and quickly saturate as models improve. To address this challenge, we propose Automated Weakness Exploration (AWE), a fully automated framework for constructing and maintaining jaggedness evaluations. Using AWE, we construct JaggedBench with 1,142 verified capability-adjacent variants and evaluate 24 state-of-the-art models. On the primary evaluation set AIME 2025, benchmark-adjacent rewrites induce an average OG-to-RW relative drop of approximately 25% in Avg@k across 24 models, with consistent degradation observed across math, code, science, and knowledge domains. We identify 18 recurring failure modes. Our analysis shows that increasing reasoning intensity and scaling model size improve average performance and reduce failure frequency, yet the underlying failure-mode categories largely persist. Overall, these findings indicate that standard benchmark scores can present an overly optimistic view of reliability near the capability boundary. JaggedBench provides a more faithful and scalable lens for evaluating boundary robustness, enabling systematic diagnosis of boundary failures and longitudinal tracking of persistent weaknesses as models evolve. We release the benchmark, code, and subsequent updates at https://jaggedbench.github.io/.

Parallel thinking offers a promising avenue for scaling test-time compute in Large Language Models (LLMs), enabling them to explore diverse solution paths simultaneously before aggregating them into a final answer. However, coordinating the exploration and aggregation stages remains challenging, as simple aggregation techniques often incur information loss, failing to preserve the subtle, decision-relevant signals generated during exploration. To overcome this, we propose Rhombus, a parallel thinking framework that explicitly incentivizes coordination between components via end-to-end reinforcement learning. Rhombus employs multiple parallel Proposers to generate compact, decision-focused reasoning cues and a central Synthesizer to integrate them into final predictions, utilizing co-training under a shared task reward to align their interaction. Across challenging mathematical reasoning benchmarks, Rhombus improves accuracy by 6.0% over long chain-of-thought baselines while reducing wall-clock latency by 39.4% under matched token budgets. Our work demonstrates that explicit communication optimization is essential for realizing the accuracy and efficiency gains of parallel reasoning.

Reinforcement Learning with Verifiable Rewards (RLVR) has propelled Large Language Models in complex reasoning, yet its scalability is often hindered by a training bottleneck where performance plateaus as policy entropy collapses, signaling a loss of exploration. While previous methods attempt to maintain high entropy, we argue that unselective entropy maximization risks amplifying irrelevant noise rather than fostering meaningful exploration. In this paper, we identify a deeper issue: the gradual elimination of valuable low-probability exploratory tokens, which we term reasoning sparks, driven by RLVR over-penalization. To address this, we introduce Low-probability Regularization (Lp-Reg). Leveraging the statistical distinction where reasoning sparks exhibit higher probabilities than noise, Lp-Reg filters out the extremely low-probability noise tokens and prevents the suppression of potentially valuable low-probability candidates. Experiments demonstrate that Lp-Reg enables stable on-policy training for over 3,000 steps (81,204 GPU-hours), sustaining exploration in regimes where baselines typically collapse. Validated across extensive evaluations totaling over 300,000 cumulative GPU-hours, Lp-Reg demonstrates highly competitive performance in off-policy settings and consistently achieves state-of-the-art results in on-policy training across diverse model families, sizes, and domains, with relative accuracy improvements ranging from 3.06% to 7.98%.

Large Language Models (LLMs) increasingly generate executable visual artifacts, yet established code benchmarks largely overlook the visual fidelity and interaction behavior that shape user experience. We introduce ArtifactsBench, a benchmark and automated multimodal evaluation framework for visual code generation. The framework executes each artifact and records three staged screenshots—before, during, and after a scripted interaction—then scores the evidence and source code with a checklist-guided Multimodal LLM (MLLM) referee. ArtifactsBench contains 1,825 tasks across nine domains and evaluates more than 30 LLMs. On a 280-query, six-model expert study, three staged screenshots yield 90.95% referee–human Pair ACC. Across the models shared with WebDev Arena, ArtifactsBench reaches 94.4% normalized Footrule consistency with that public human-preference ranking. We release the benchmark, evaluation harness, and baseline results at https://artifactsbenchmark.github.io/.

Recent progress in large language models (LLMs) is largely driven by scaling training compute through either pre-training with next-token prediction (NTP) or post-training with reinforcement learning (RL). The former contributes to learning broad knowledge and skills from general data, while struggling with data inefficiency and catastrophic forgetting in continual learning settings. The latter incentivizes reasoning capabilities with strong generalization, but is constrained by limited data availability due to its reliance on human annotation. To alleviate these issues, we propose Reinforcement Learning on Pre-Training data (RLPT), which combines the advantages of learning from general data and RL. In particular, RLPT derives reward signals directly from general text data through a next-segment reasoning objective, rewarding the policy for correctly predicting next text segments conditioned on the prefix text. Experiments across multiple benchmarks and models demonstrate the effectiveness of . For example, RLPT yields substantial improvements in continual pre-training (+4.6%) and provides a strong foundation for post-training (+3.4%) on Qwen3-8B-Base.

We introduce PaSa, an advanced Paper Search agent powered by large language models. PaSa can autonomously make a series of decisions, including invoking search tools, reading papers, and selecting relevant references, to ultimately obtain comprehensive and accurate results for complex scholar queries. We optimize PaSa using reinforcement learning with a synthetic dataset, AutoScholarQuery, which includes 35k fine-grained academic queries and corresponding papers sourced from top-tier AI conference publications. Additionally, we develop RealScholarQuery, a benchmark collecting real-world academic queries to assess PaSa performance in more realistic scenarios. Despite being trained on synthetic data, PaSa significantly outperforms existing baselines on RealScholarQuery, including Google, Google Scholar, Google with GPT-4o for paraphrased queries, ChatGPT (search-enabled GPT-4o), GPT-o1, and PaSa-GPT-4o (PaSa implemented by prompting GPT-4o). Notably, PaSa-7B surpasses the best Google-based baseline, Google with GPT-4o, by 37.78% in recall@20 and 39.90% in recall@50, and exceeds PaSa-GPT-4o by 30.36% in recall and 4.25% in precision. Model, datasets, and code are available at https://github.com/bytedance/pasa.Demo: https://pasa-agent.ai

The widespread use of large language models (LLMs) has sparked concerns about the potential misuse of AI-generated text, as these models can produce content that closely resembles human-generated text. Current detectors for AI-generated text (AIGT) lack robustness against adversarial perturbations, with even minor changes in characters or words causing a reversal in distinguishing between human-created and AI-generated text. This paper investigates the robustness of existing AIGT detection methods and introduces a novel detector, the Siamese Calibrated Reconstruction Network (SCRN). The SCRN employs a reconstruction network to add and remove noise from text, extracting a semantic representation that is robust to local perturbations. We also propose a siamese calibration technique to train the model to make equally confident predictions under different noise, which improves the model’s robustness against adversarial perturbations. Experiments on four publicly available datasets show that the SCRN outperforms all baseline methods, achieving 6.5%-18.25% absolute accuracy improvement over the best baseline method under adversarial attacks. Moreover, it exhibits superior generalizability in cross-domain, cross-genre, and mixed-source scenarios. The code is available at https://github.com/CarlanLark/Robust-AIGC-Detector.

In document-level event extraction (DEE) tasks, a document typically contains many event records with multiple event roles. Therefore, accurately extracting all event records is a big challenge since the number of event records is not given. Previous works present the entity-based directed acyclic graph (EDAG) generation methods to autoregressively generate event roles, which requires a given generation order. Meanwhile, parallel methods are proposed to generate all event roles simultaneously, but suffer from the inadequate training which manifests zero accuracies on some event roles. In this paper, we propose an Iteratively Parallel Generation method with the Pre-Filling strategy (IPGPF). Event roles in an event record are generated in parallel to avoid order selection, and the event records are iteratively generated to utilize historical results. Experiments on two public datasets show our IPGPF improves 11.7 F1 than previous parallel models and up to 5.1 F1 than auto-regressive models under the control variable settings. Moreover, our enhanced IPGPF outperforms other entity-enhanced models and achieves new state-of-the-art performance.