People increasingly hand fact-finding to LLM agents that search the web and return a cited report. We ask whether the belief a user states when making the request changes the research itself. We give 48 expert-verified health claims to four search-equipped agents under believer, skeptic, and neutral framings, analyzing 1,874 of 2,064 registered runs plus second-round arms reported separately. The effect is asymmetric: stating belief leaves conclusions unchanged (0.02, claim-clustered 95% CI -0.04 to 0.09), while stating doubt moves them toward rejection (-0.26, CI -0.39 to -0.15). Across opposing framings the same agent’s conclusion shifts 0.26 points on a five-point scale (CI 0.15 to 0.40), a quarter of one label step, and every system moves the same way. The shift sits on contested claims (0.61) and is small on the settled tiers (0.10 and 0.11). Strictly opposite conclusions were rare and their registered paired test was null (p=0.250), so this is a shift in degree rather than a reversal of verdicts. The trajectories mirror it: skeptic framing more than doubles deny-seeking queries (net -9.9 points against +0.5 for believer framing). Crossing the retrieved evidence between framings splits the gap into how the agent reads what comes back (0.23, CI 0.12 to 0.34) and which evidence comes back at all (0.08, CI 0.00 to 0.16), two channels that add to the end-to-end gap by construction and put about three quarters of it on reading, though the evidence interval reaches zero. Labeling retrieved results against the claim finds no evidence that stated doubt returns a more hostile pool (1.7 points, CI -1.5 to 5.5). An indicative post-hoc follow-up on the 32 claims it reached adds a both-sides instruction to the skeptic condition, where the bias lives, and shifts conclusions by 0.16 (CI 0.04 to 0.30). We release FramedFacts-48: claims, prompts, and analyzed runs.

Grounded language-model pipelines can be divided into three stages: selecting an object, retrieving passages for it, and using that evidence to answer. If the selected object must reach the reader, losing it breaks the handoff. Benchmark recall checks the dataset-linked object, which can differ. We audit 600 HybridQA questions across three selector families. On 1,463 resolvable records where the selected object matches the dataset-traced passage, exact key lookup and exact title matching return the object every time. With every ranked rule given the same decoded selected title, body-only BM25 omits it on 389 records (26.6%) at cutoff five, while hybrid retrieval with reranking omits it on 14 (1.0%). The two identities differ on 329 of 1,792 resolvable records. With original-question rankings, their top-five checks disagree on 106 records (5.9%). Frozen reader comparisons associate the aligned object’s presence with 28.6 to 31.0 points higher exact match. In a deliberately selected 64-item cohort, removing that passage sharply lowers exact match, while removing a similar-length comparison passage does not reproduce the drop. We release the Returned-Object Profile (ROP), an executable record of the target, returned-ID field, cutoff, membership rule, and complete expected population, with data and an offline replay.

LLM-based scientific agents have shown strong capacity for autonomous research, yet their safety layers remain structurally divorced from core reasoning: they inspect pipeline outputs rather than shaping the deliberation that produces them. This separation opens two failure modes: safety signals accumulated at one stage are discarded before the next, and sequences of individually benign tool calls can compose into harmful outcomes that no single-step filter detects. To address these challenges, we introduce **SciTrace**, a framework that weaves safety reasoning into every stage of the scientific agent pipeline. SciTrace couples two complementary mechanisms: a *Safety-Intrinsic Reasoning Loop* (SIR) that maintains a cumulative risk state across the Thinker, Experimenter, Writer, and Reviewer stages through joint task-and-safety deliberation, and a *Compositional Tool-Chain Verifier* (CTV) that performs trajectory-aware safety checks before execution, catching risks that surface only across multi-step tool sequences. Evaluated on 240 high-risk research tasks and 120 tool-related risk tasks spanning six scientific domains, SciTrace achieves state-of-the-art (**SOTA**) safety among compared frameworks across four backbone models: it consistently improves tool call safety and adversarial robustness while preserving scientific output quality, and it uncovers **78.8%** of the compositional tool-chain escapes that single-step monitors miss. The project website is available at [https://opensciagent.github.io/SciTrace/](https://opensciagent.github.io/SciTrace/).

We introduce Evo, a duality latent trajectory model that bridges autoregressive (AR) and diffusion-based language generation within a continuous evolutionary generative framework. Rather than treating AR decoding and diffusion generation as separate paradigms, Evo reconceptualizes text generation as a latent flow: each token is associated with a vector-valued embedding that evolves over a progression variable, indicating its semantic maturity. Low values correspond to confident AR-like refinement, while high values invoke diffusion-style planning, allowing the model to adaptively balance AR and diffusion based on uncertainty. Theoretically, we show that both AR and diffusion models emerge as discretizations of a shared probability flow, and we derive Evo’s training objective from a unified variational ELBO. The model is implemented as a time-conditioned Transformer governed by a shared vector field, trained end-to-end to jointly infer latent codes and their progression times. During decoding, Evo performs efficient, semantics-aware refinement, achieving high-quality outputs without sacrificing speed. Empirically, Evo 8B achieves state-of-the-art or highly competitive results on 15 diverse benchmarks, including reasoning (GSM8K, ARC-C), code generation (HumanEval, MBPP), and general language understanding, while maintaining fast inference speed. Our results demonstrate that Evo delivers a new paradigm for LLM design with strong generation quality, robust symbolic reasoning, and decoding efficiency.

We introduce Agentic Reasoning, a framework that enhances large language model (LLM) reasoning by integrating external tool-using agents. Agentic Reasoning dynamically leverages web search, code execution, and structured memory to address complex problems requiring deep research. A key innovation in our framework is the Mind-Map agent, which constructs a structured knowledge graph to store reasoning context and track logical relationships, ensuring coherence in long reasoning chains with extensive tool usage. Additionally, we conduct a comprehensive exploration of the Web-Search agent, leading to a highly effective search mechanism that surpasses all prior approaches. When deployed on DeepSeek-R1, our method achieves a new state-of-the-art (SOTA) among public models and delivers performance comparable to OpenAI Deep Research, the leading proprietary model in this domain. Extensive ablation studies validate the optimal selection of agentic tools and confirm the effectiveness of our Mind-Map and Web-Search agents in enhancing LLM reasoning. Our code and data are publicly available.

We introduce MedGraphRAG, a novel graph-based Retrieval-Augmented Generation (RAG) framework designed to enhance LLMs in generating evidence-based medical responses, improving safety and reliability with private medical data. We introduce Triple Graph Construction and U-Retrieval to enhance GraphRAG, enabling holistic insights and evidence-based response generation for medical applications. Specifically, we connect user documents to credible medical sources and integrate Top-down Precise Retrieval with Bottom-up Response Refinement for balanced context awareness and precise indexing. Validated on 9 medical Q&A benchmarks, 2 health fact-checking datasets, and a long-form generation test set, MedGraphRAG outperforms state-of-the-art models while ensuring credible sourcing. Our code is publicly available.