arXiv subjects

Yi Feng

Publications and source records attributed to Yi Feng.

At least 19 recordsLinked to original sources

arXiv2026

Earth-Agent-Pro: Towards Real-World Full-Chain Earth Observation with Agents

Real-world Earth observation (EO) agents must translate high-level scientific questions into executable workflows to acquire observations, prepare data, perform domain computations, and derive conclusions from runtime evidence. Existing EO agents typically start from supplied observations, while benchmarks typically provide prepared inputs or candidate answers, leaving full-chain open-world EO execution largely untested. We present Earth-Agent-Pro, an execution-adaptive Plan-and-Execute framework using expert-authored skills to constrain planning and runtime tool use. Workflow-centered structured memory records planned steps, accepted evidence, and their dependencies, enabling repair of only the affected workflow suffix when runtime evidence invalidates a step. Separate large language model adapters use sequence-level supervised fine-tuning for planner workflow composition and node-level group relative policy optimization with locally verifiable rewards for executor tool-argument grounding. Earth-Bench-Pro instantiates 248 expert-curated task cores as 744 questions under three matched regimes. Its 248 Open-World Execution questions span RGB imagery, spectral observations, and remote sensing products, pairing high-level requests with runtime data requirements, executable trajectories, and open-ended answers grounded in execution evidence. With a shared GPT-5 backbone, Earth-Agent-Pro achieves 66.13% LLM-as-Judge accuracy, exceeding ReAct by 20.95 points in this metric and 24.44 points in Tools-In-Order. Joint adapter tuning raises Qwen3.5-9B LLM-as-Judge accuracy from 38.31% to 50.00%, an 11.69-point gain over the untuned configuration. Planning-only evaluation and execution with the reference workflow show that the adapters improve workflow composition and argument grounding, respectively. Code and datasets will be released soon.

cs.CV↗

arXiv2026

Fast radio bursts, magnetars and earthquakes: their "family feud"?

Fast radio bursts (FRBs) are millisecond-duration cosmic transients whose origin remains elusive. Competing models invoke either earthquake-like processes or flare-like mechanisms. To discriminate between these scenarios, we develop a novel diagnostic, the Pincus-Lyapunov diagram (PLD), to characterize the energetic transients in the stochasticity-chaos phase space. We compile burst sequences from five representative FRBs (FRB 20121102A, FRB 20190520B, FRB 20201124A, FRB 20220912A, and FRB 20240114A), together with those from magnetar flares (SGR J1550$-$5418, SGR J0501+4516, SGR 1806$-$20, SGR 1900+14, and SGR J1935+2154), pulsar glitches, solar flares, and earthquakes, and map them onto the PLD for comparative analysis. The resulting diagram shows that FRBs occupy a distinct region of the phase space. Specifically, a permutation test reveals a statistically significant difference in the distributions of magnetar flares and pulsar glitches compared to those of repeating FRBs ($p$-value $\simeq 0.05$). To examine whether temporal variations in source activity can shift a repeater's position in this phase space, we analyze the time evolution of the most prolific repeater, FRB~20240114A. For this repeating FRB, both Pincus Index and Lyapunov Exponent demonstrate statistically stable behaviour over the eight-month observation session, with Augmented Dickey--Fuller tests yielding $p \simeq 1.78\times10^{-3}$ and $9.91\times10^{-3}$, respectively. By assembling the most comprehensive dataset to date, our work indicates that the trigger mechanisms of repeating FRBs are likely to be distinct from those driving magnetar flares, pulsar glitches, solar flares, and earthquakes.

astro-ph.HE↗

arXiv2026

Spectro-Polarimetric Properties of CHIME FRB Sources

Fast radio bursts (FRBs) are enigmatic millisecond-duration radio transients whose polarization properties offer crucial insights into their origins and environments. In particular, low-frequency depolarization---quantified by the parameter $σ_{\mathrm{RM}}$---probes the complex magneto-ionic medium surrounding the progenitor, and has been observed across a population of repeating FRBs. We present a systematic spectro-polarimetric analysis of repeating and non-repeating FRBs using observations from the Canadian Hydrogen Intensity Mapping Experiment (CHIME). For 28 repeating FRBs, we measure $σ_{\mathrm{RM}}$, expanding the known sample from 14 to 36 sources (an increase by a factor of 2.6). The kernel density estimate (KDE) of the repeating population peaks at $1.3\ \mathrm{rad\,m^{-2}}$, with approximately 70\% of the sources showing $σ_{\mathrm{RM}} \gtrsim 1\ \mathrm{rad\,m^{-2}}$, implying that most reside in complex magneto-ionic environments. For 70 non-repeating FRBs, we investigate four spectro-polarimetric models; no source exhibits significant depolarization with $σ_{\mathrm{RM}} \gtrsim 5\ \mathrm{rad\,m^{-2}}$. Roughly half of the non-repeaters are consistent with a constant linear polarization fraction across frequency. We caution, however, that these results may be affected by the limited frequency coverage of CHIME. Future ultra-wideband polarimetry, spanning widely separated frequencies, will overcome current observational biases, enable precise $σ_{\mathrm{RM}}$ measurements, and substantially deepen our understanding of FRB environments.

astro-ph.HE↗

arXiv2026

The Uncontrolled Variable: Vision-Language Model Refusal Responds to Image Presence in Ways Risk Cannot Explain

Vision-Language Model (VLM) safety is expected to depend on what a request asks for. We show that safety-aligned VLMs also key refusal on a property of a request's form: whether an image is attached, holding everything the request asks fixed. Attaching a blank canvas - unreadable, unrelated to the request, identical across prompts - shifts refusal by tens of percentage points, with no defense in the loop. The shift is not blanket caution. Neutral instructions are almost unaffected while borderline-benign prompts move sharply, so the cost falls on sensitivity-adjacent traffic: benign questions about privacy, self-harm and violence. Attachment alone is sufficient, while the image's properties set the price: a black canvas costs substantially more than a white one of identical size, and on an open checkpoint the carrying axis is pixel count. Nor is the shift under instructional control - telling the model the image is a placeholder to be disregarded removes only a fraction of it, and on one model asserting that an attachment exists moves refusal substantially with nothing attached. Attachment may correlate with risk in deployment; what these models do with it does not track risk. It is not the serving stack, since the same weights reached two ways behave alike, nor a property of VLMs as such, since several open-weight checkpoints show nothing. It belongs to particular aligned checkpoints, one of them open. It is also decoupled from what it buys: the canvas does prevent some attack success on a matched harmful set, but far less than it costs, and its sign is not fixed - on one open model the identical canvas makes the model markedly easier to attack. Image presence is not a default a deployer chose or priced; it is an uncontrolled variable inherited with the weights.

cs.CR↗

arXiv2026

Unread or Unenforced? Separating Representation from Enforcement Failure in Content Guards

When an encoded attack passes a content guard, the guard either never represented the payload's harmful content or represented it and failed to act. End-to-end attack success rate reports one number for both, yet the remedies are opposite: one is a representational limit that more safety training cannot reach, the other a decision rule that it can. We separate them by reading a guard's own residual stream -- a content probe fitted on plaintext and transferred, without refitting, to the encoded condition -- alongside its verdict logits, at the cost of one forward pass and no judge model. Doing this honestly is most of the problem, and it is our main contribution. A permutation test licenses the decode measurement on 17 of 19 conditions for one open guard and 12 of 19 for another; a length-matched null and a control floor calibrated on conditions the guard's base model provably cannot decode reduce both to 4. The discarded cells are not marginal ones: the largest result in our first analysis -- a guard representing a cipher at AUROC 0.72 while blocking none of it -- is an artefact on an encoding its base model decodes at rate zero. On one guard, two screens sharing no input agree exactly on which conditions to reject. What survives is a policy failure that is real but narrower than the uncontrolled analysis claimed: 7 to 23 per 100 prompts represented and not blocked on conditions the guards block heavily, and 56 per 100 on one condition a guard barely blocks. Blocked without decoding is near zero throughout, so neither guard reacts to the appearance of encoding rather than to content. On genuine ciphers both guards block essentially nothing, and we report those cells as unmeasured rather than as evidence of failure to decode.

cs.CR↗

arXiv2026

OpenVisTool: An Open Recipe for Synthesizing Instructive Visual Tool-Use Trajectories

Visual tool use has emerged as a fundamental capability for multimodal agents to actively acquire evidence beyond a fixed image encoding. The prevailing recipe learns this capability from teacher-generated trajectories filtered for answer correctness, implicitly assuming that every successful demonstration provides effective supervision. We argue this assumption is flawed: a strong teacher often reaches the correct answer without needing its tool calls, and imitating such trajectories teaches a student that tool calls accompany correct answers, not that tool observations ground them. We present OpenVisTool, an open framework for constructing instructive visual tool-use trajectories that provide effective supervision for tool learning. The key insight is that a trajectory should be retained only if its answer is correct (outcome validity) and its tool observations causally contribute to that answer (causal utility). The framework operates in three stages: difficulty screening to select queries that are not reliably answerable without tools, domain-specific trajectory synthesis to elicit coherent tool-use trajectories, and supervision verification to jointly test both conditions. Rather than encouraging models to imitate tool calls, the resulting supervision teaches when and how visual evidence should be acquired. Using this framework, we construct OpenVisTool-42K, a dataset spanning five visual reasoning domains, together with OpenVisTool-Bench, a benchmark covering the same domains. Across four backbones (4B-27B), fine-tuning on OpenVisTool-42K consistently improves visual tool-use performance and yields gains on two out-of-distribution benchmarks; the larger models approach leading closed-source systems. The evidence suggests that effective visual tool use is learned from causally grounded supervision rather than tool-calling patterns.

cs.CL↗

arXiv2026

Attack Ensembles Expose a Safety-Utility Trade-off in Black-Box Guard Defenses Against Encoded VLM Jailbreaks

Safety classifiers ("guards") are the dominant black-box defense for vision-language models, yet a guard judges an input's surface form, not its meaning: a harmful request re-encoded as set theory, formal logic, a classical language, code, or text rendered inside an image slips past a guard that would block it in plain language - the decode gap. The standard fix is a preprocessor that recovers image content and decodes the encoding before the guard. We build one and evaluate it against an ensemble of eleven published encoding attacks, counting a behavior as broken if any attack succeeds. That metric separates two mechanisms such defenses conflate. Restoring a view the guard never had improves it on both axes at once: it blocks far more attacks, and, measured on a category-balanced benign set, it blocks fewer benign requests, because restating a request normalizes the borderline phrasing a classifier over-flags. It still does not make the system safer: against an attacker free to choose among eleven encodings, closing one channel relocates the success rather than removing it, and no ensemble contrast survives multiple-comparison correction. What does lower ensemble attack success is re-screening the recovered pre-decode surface, and that step is where the entire benign cost falls. The safety-utility trade-off is therefore not a property of recovery; it is localized to one step. Across the full guard x target x condition factorial, no configuration reaches an ensemble attack-success rate at or below 40% while holding benign over-refusal under 70%. The per-attack averages usually reported understate the attacker roughly fourfold, which is why this frontier is easy to miss. Composing across defense families is the one lever that moved the safety axis, beating every configuration we measured, and still landing far outside any deployable refusal budget.

cs.CR↗

arXiv2026

Whose Refusal Is It? The Unmeasured Contribution of Black-Box Multimodal Guardrails

A black-box guardrail is evaluated as though the safety number it earns were its own. It is not. A defended pipeline holds two components that can refuse (the guardrail, and the target model out of its own alignment), and every reported metric is a sum over both. We show that the guardrail's actual share of the safety credited to it runs from none of it to essentially all of it, decided by two variables no evaluation records: which channel carries the payload, and what text the harness places in the defense's internal read. The split is recoverable at no extra cost, because a guard block replaces the model's response and the two counts are therefore disjoint. On a text guard across two open-weight targets: with the payload rendered as pixels the guard blocks nothing and the model produces every refusal the system makes; reading the encoded prompt the attacker actually sent, the guard produces a minority of the refusals attributed to it; reading the unencoded request behind the attack, it blocks almost everything and the model falls silent. The blindness is not inaccuracy: the same guards block no benign image inputs either, so their image-channel decision is a constant. Granting the unencoded request inflates measured benefit substantially for a guard gate, less for a caption-mediated re-check, and not at all for a majority-vote smoother; the ordering reproduces in an independent replicate. Isolating the grant within one defense shows it does not improve detection: the harm-verdict stage contributes nothing, while the stage that regenerates the answer carries the effect. Nor is the inflated setting careless; the reference implementation builds every stage from a single prompt field that cannot distinguish what the attacker sent from what the benchmark records, so faithful porting supplies it silently. Previously published figures of our own are among those revised.

cs.CR↗

arXiv2026

Agentic Reinforcement Learning with Observation-Calibrated Self-Distillation

Large language model agents are commonly trained through reinforcement learning with sparse trajectory-level rewards, which offer limited guidance on how strongly individual tokens should be updated. On-Policy Self-Distillation (OPSD) addresses this by re-scoring generated tokens under a privileged replay view to obtain dense, token-level supervision. However, we identify a confounding issue: the resulting support may reflect both the privileged information contained in the replay view and score shifts induced by the replay scaffold, making it difficult to attribute the support specifically to that information. This issue is especially pronounced when future environment observations serve as privileged information, since replaying them requires reconstructing an extended scaffold that itself perturbs token scores. To resolve this confounding, we propose Observation-Calibrated Self-Distillation (OCSD), which contrasts two structurally matched replay views, Full and Observation-Ablated, differing only in whether the actual future observation is present, to derive an observation residual that discounts score changes shared by the replay scaffold. OCSD then applies this residual to modulate token-level GRPO updates at high-uncertainty steps, while preserving the trajectory-level update direction. Experiments on ALFWorld, WebShop, and Search-QA across three Qwen3 model scales show that OCSD consistently outperforms strong baselines. Diagnostic analyses further confirm that the calibrated residual aligns better with local environment feedback. Our code is publicly available at https://github.com/yiy1x/OCSD.

cs.LG↗

arXiv2026

Rethinking Scientific Discovery in the Agentic Era

Artificial intelligence has advanced scientific discovery, but most AI4Science systems remain fragmented tools that rely on humans to coordinate problem formulation, literature grounding, model use, simulation, validation, and knowledge reuse. This paper presents \textbf{SCION (Scientific Collaborative Innovation with Agentic Organizational Nexus)}, an agentic scientific operating system that acts as an \textbf{organizational nexus}. Through a Science Agent serving as a \textbf{Meta-Harness}, SCION connects scientific tasks, tools, agents, artifacts, and memory, transforming research into an executable, auditable, and reusable operational process. At its core is the \textbf{Research Execution Plan (REP)}, which compiles high-level scientific intent into staged objectives, dependencies, verification checkpoints, tool requirements, expected artifacts, and fallback conditions. SCION further integrates hierarchical multi-agent execution, profile-driven specialization, selective context construction, governed delegation, and layered epistemic memory to support long-horizon scientific work. We formulate discovery under SCION as \textbf{Target-conditioned Inverse Search} and extend it to hidden-target settings through batch active search under finite experimental budgets. Applications in materials analysis, molecule design, and protein or antibody screening, together with experiments on scientific reading, idea generation, molecule generation, and antibody screening, show that SCION outperforms existing autonomous research-agent baselines, especially in decomposition, verification, refinement, and memory reuse. Overall, SCION shifts AI from isolated tools toward a coordinated operational layer for traceable and reusable scientific innovation.

cs.CL↗

arXiv2026

No Strong Evidence for Plasma Lensing in FRB 20240114A

FRB~20240114A is an extremely active repeating fast radio burst for which plasma lensing has been proposed to explain its burst-rate variations, spectral evolution, and apparently ``carbon-copy'' burst pairs. Using FAST data and publicly available Parkes observations, we test this interpretation with a one-dimensional Gaussian plasma-lens model. Although the burst-rate enhancements can be fitted separately, the corresponding magnification peaks and demagnification troughs are offset by far more than predicted and show no consistent periodicity. Moreover, with more than 10,000 bursts detected, a few apparently ``carbon-copy'' pairs can readily occur by chance. The burst bandwidth is not systematically narrower during the proposed lensing interval, nor are the burst energies significantly enhanced during the predicted magnification interval. These results provide no compelling evidence that a single Gaussian plasma lens explains the observed variability, which is more likely dominated by intrinsic source activity.

astro-ph.HE↗

arXiv2026

A pulsar escaping an ancient open cluster via tidal stripping

Open clusters are the primary birthplaces of stars in the Milky Way disk, yet their neutron star progeny are rarely found within them, presumably due to supernova-induced kicks that eject them at birth. Here we report the arcsec-level localization of the pulsar PSR J1921+3745 to the tidal tail of NGC 6791, one of the oldest and most massive open clusters. Our N-body simulation shows that more than 95% of neutron stars formed in such clusters have been ejected. This pulsar's location in the tidal tail indicates it was retained for billions of years before being stripped by Galactic tides. This long-term retention requires low natal kicks, consistent with formation via electron-capture supernova. Our findings capture a rare snapshot of a neutron star transitioning into the Galactic field, identifying tidal stripping of ancient clusters as a verified source of the Galactic neutron star population.

astro-ph.HE↗

arXiv2026

The Real and Pseudo Dispersion Measures of FRB~20220912A

Fast radio bursts (FRBs) are millisecond-duration radio transients. As they propagate through the interstellar medium, they interact with free electrons, resulting in dispersion. The corresponding dispersion measure (DM) is referred to as the real DM (DM$_{\rm real}$). In practice, however, the dispersion measure derived from modeling (DM$_{\rm model}$) is often contaminated by intrinsic burst morphology, giving rise to a pseudo DM component (DM$_{\rm pseudo} = {\rm DM}_{\rm model} - {\rm DM}_{\rm real}$). In this work, we focus on the highly active repeating FRB~20220912A and utilize its microshots -- extremely short-duration (typically tens of microseconds), broadband emissions -- to investigate its DM$_{\rm real}$ and DM$_{\rm pseudo}$. We adopt two assumptions: first, that FRB~20220912A resides in a non-magneto-ionic environment and that its DM$_{\rm real}$ variation is smaller than $10^{-2}$\,pc\,cm$^{-3}$ over a few years; and second, that microshots have a negligible intrinsic morphological time delay. By identifying two new microshots and combining them with previously reported ones, we find that all four microshots exhibit remarkably consistent DM values over a one-month timescale, with an average of $219.380 \pm 0.004\,\mathrm{pc\,cm^{-3}}$. We define this value as the DM$_{\rm real}$ of FRB~20220912A. We further show that bright, narrow bursts with a width of less than 2\,ms also yield DM estimates consistent with the microshot-based DM$_{\rm real}$. A survey of five repeating FRBs reveals that DM$_{\rm pseudo}$ is a common phenomenon, with variations typically spanning a range of approximately $10\,\mathrm{pc\,cm^{-3}}$ at 1.2\,GHz. These findings highlight the importance of accounting for morphological contributions in DM interpretation and demonstrate that microshots and narrow bursts are powerful tools for probing DM$_{\rm real}$.

astro-ph.HE↗

arXiv2026

Flux Variations of Fast Radio Bursts and Their Persistent Radio Sources: Evidence for a Shared Progenitor

Fast radio bursts (FRBs) are millisecond-duration extragalactic radio transients, some of which are associated with compact persistent radio sources (PRSs), hinting at a physical connection. While several models have been proposed to explain PRSs and their connection to FRBs, direct observational tests remain limited. Here, we report for the first time a correlated trend between the long-term variation of the PRS flux density and the burst energetics of FRB 20190520B and FRB 20240114A, suggesting a physical coupling between the PRS and FRB activity. We further examine additional repeaters with compact PRSs and find no clear correlation between PRS luminosity and burst activity, likely due to the limited observations. These results are consistent with scenarios in which both the PRS and FRB activity may be powered by a common energy reservoir, such as the magnetic or rotational energy of a magnetar.

astro-ph.HE↗

arXiv2026

LineageFlow: Flow Matching for High-Fidelity Family-Aware Protein Sequence Generation

Protein sequence generation for engineering requires samples that are biophysically plausible and, when targeting a family/domain, remain recognizable members while exploring within-family diversity. Current discrete generative models typically start from uniform or masked-token noise, which discards strong position-specific constraints induced by evolution and forces the model to reconstruct conserved residues from scratch, leading to weak family control and low plausibility. We propose \emph{LineageFlow}, a Dirichlet flow-matching model that initializes generation from lineage priors derived from ancestral sequence reconstruction, turning generation into structured mutation from an evolved scaffold. Across diverse protein families, LineageFlow achieves family validity close to held-out natural sequences and improves predicted structural confidence over uniform-/mask-initialized baselines while maintaining substantial novelty and diversity. Finally, we introduce \emph{rerouting}, a single intermediate-time mutate--select--amplify intervention that enables objective-guided sampling without per-step predictor guidance and yields further gains in plausibility, including a zero-shot enzyme generation case study. Code is available at https://github.com/Jinx-byebye/LineageFlow.

cs.CE↗

arXiv2026

Understanding Dynamics of Adam in Zero-Sum Games: An ODE Approach

The remarkable success of the Adam in training neural networks has naturally led to the widespread use of its descent-ascent counterpart, Adam-DA, for solving zero-sum games. Despite its popularity in practice, a rigorous theoretical understanding of Adam-DA still lags behind. In this paper, we derive ordinary differential equations (ODEs) that serve as continuous-time limits of the Adam-DA. These ODEs closely approximate the discrete-time dynamics of Adam-DA, providing a tractable analytical framework for understanding its behavior in zero-sum games. Using this ODE approach, we investigate two fundamental aspects of Adam-DA: local convergence and implicit gradient regularization. Our analysis reveals that the roles of the first- and second-order momentum parameters in zero-sum games are exactly the opposite of their well-documented effects in minimization problems. We validate these predictions through GAN experiments across multiple architectures and datasets, demonstrating the practical implications of this reversed momentum effect.

cs.LG↗

arXiv2026

The Midas Touch for Metric Depth

Recent advances have markedly improved the cross-scene generalization of relative depth estimation, yet its practical applicability remains limited by the absence of metric scale, local inconsistencies, and low computational efficiency. To address these issues, we present \emph{\textbf{M}idas \textbf{T}ouch for \textbf{D}epth} (MTD), a mathematically interpretable approach that converts relative depth into metric depth using only extremely sparse 3D data. To eliminate local scale inconsistencies, it applies a segment-wise recovery strategy via sparse graph optimization, followed by a pixel-wise refinement strategy using a discontinuity-aware geodesic cost. MTD exhibits strong generalization and achieves substantial accuracy improvements over previous depth completion and depth estimation methods. Moreover, its lightweight, plug-and-play design facilitates deployment and integration on diverse downstream 3D tasks. Project page is available at https://mias.group/MTD.

cs.CV↗

arXiv2026

Energy equality of the weak solutions to non-Newtonian fluids equations

In this paper, we study the problem of energy equality for weak solutions of the 3D incompressible non-Newtonian fluid equations with initial value conditions. We derive new sufficient conditions via Sobolev multiplier spaces that guarantee the validity of the energy equality. Moreover, the aforementioned equations are often associated with the uniqueness problem of weak solutions for non-Newtonian fluids, which, in a certain sense, constitutes the positive counterpart of Onsager's conclusion for non-Newtonian fluids.

math.AP↗