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Liang Chen

Publications and source records attributed to Liang Chen.

At least 19 recordsLinked to original sources
arXiv2026

PAPERBENCHX: Can AI Reproduce Science Across Disciplines?

Before AI can be trusted to discover new science, it should be able to reproduce what science already knows. Reproduction provides a rigorous checkpoint for an AI scientist: the target claim is known, yet recovering it still requires translating an incomplete scientific description into an executable experiment and producing evidence that can be independently verified. Existing research-reproduction benchmarks largely focus on AI and machine learning, where experiments share relatively homogeneous software stacks. Extending this capability across scientific disciplines introduces a different challenge: agents must operate domain-specific scientific software, make physical and numerical choices, diagnose long-running computations, and distinguish valid evidence from merely plausible outputs. We introduce PAPERBENCHX, a benchmark of 93 expert-constructed paper-reproduction tasks spanning 12 research areas across electromagnetics, photonics, chemistry, materials science, biology, and robotics. Each task provides the source paper, a public task specification, and a domain-native scientific environment involving specialized scientific software. Agents must reconstruct a key experiment, execute and diagnose it, and deliver a runnable workflow that supports the target scientific claim. To evaluate scientific validity rather than surface-level agreement, we replay every submission from scratch and score only regenerated evidence against expert-validated criteria. Across 10 frontier agent configurations, the best configuration fully reproduces only 14% tasks under the 90% threshold. Meanwhile, mean rubric scores range from 27.53% to 59.08%, indicating that substantial partial progress rarely translates into complete scientific reproduction. These results reveal a large gap between paper understanding and reliably producing reproducible scientific evidence.

cs.AI↗
arXiv2026

A Candidate Binary Microlensing Event in the 2018 Gamma-Ray Flare of 3C 279

The April 2018 gamma-ray flare of the flat-spectrum radio quasar 3C~279 exhibited sharp peaks within a broader outburst. Its rapid substructure was previously attributed to magnetic reconnection. Motivated by its distinctive temporal profile within our comparison sample of bright blazar flares and its resemblance to caustic-crossing features in optical microlensing events, we investigate a finite-source binary-microlensing interpretation. A jointly fitted model of binary-lens magnification, a smooth intrinsic flare, and Gaussian-process variability reproduces the principal double-peaked structure of the GeV light curve. The adopted local solution has a binary mass ratio of $q\simeq0.098$ and an Einstein timescale of $t_{\rm E}\simeq0.327$~d. The absolute lens mass and projected source radius depend on the lens distance and relative proper motion. The fitted geometry can correspond to a Galactic planetary-mass binary or an intervening stellar or substellar system, with different physical source sizes in each case. We present binary microlensing as a testable interpretation of this event, with the finite-source structure offering a potential probe of the compact GeV-emitting region.

astro-ph.HE↗
arXiv2026

Numerical Simulations of the interaction between winds from tidal disruption event and supermassive black hole circumnuclear medium

The interaction between winds from tidal disruption event (TDE) and the circumnuclear medium (CNM) around supermassive black holes is believed to contribute to the radio emission of non-jetted TDEs. This model has been studied both analytically and numerically. In previous works, the properties of wind are usually set to be angle-independent. It is not clear to what extent the results deviate from those in the case where TDE wind properties are angle-dependent. We perform numerical simulations to investigate the effects of the angular distribution of wind properties on the radio light curve in the wind-CNM interaction model. We find that given that the wind mass and kinetic energy fixed, the radio light curve in the optically thin waveband significantly depends on the angle-dependence of wind properties. Even when the swept-up CNM mass is still less than the wind mass, a slight deceleration can affect the light curve. Changing the angular dependence of the wind properties causes the deceleration to vary across angles. Consequently, the final light curve--the time evolution of radiation integrated over all angles--would differ significantly from that predicted in models with a spatially uniform wind injection. This can result in a slower rise or a faster decline (depending on the CNM profile) than the analytical prediction, and can even turn the rise phase into a long-lasting plateau. Therefore, it is essential to carefully take into account the angular dependence of wind properties to study the wind-CNM interaction.

astro-ph.HE↗
arXiv2026

Composition factors and finite representation type

We prove that an Artin algebra has finite representation type if its indecomposable finitely generated modules are determined, up to isomorphism, by their composition factors. This gives an affirmative answer to the conjecture on composition factors and finite representation type recorded by Auslander, Reiten and Smalo.

math.RT↗
arXiv2026

Convergence Analysis of Variable-Separation-Based Model Reduction for Linear Parametric Partial Differential Equations

This paper analyzes the convergence of a variable-separation (VS) model reduction method that successively constructs separated approximations to the solutions of linear parametric partial differential equations (PDEs) by solving residual problems at sampled parameter values. We prove that the resulting VS approximations converge almost surely to the exact parametric solution in the parameter-averaged energy norm, under finite-dimensional approximation conditions on the error family and the assumptions of symmetry, uniform coercivity, and uniform boundedness of the governing bilinear forms. For parametric PDEs whose operators and right-hand sides admit finite-term separated representations, we construct finite-dimensional approximation spaces for the error family and bound their dimensions and approximation errors, thereby verifying these approximation conditions and obtaining an $O((\log N)^{-1})$ upper bound for the expected parameter-averaged error energy after $N$ iterations. When the solution families are contained in a fixed finite-dimensional space, we obtain the sharper $O(N^{-1})$ bound for the expected error energy and, if the operator is also parameter-independent, exact recovery for almost every parameter after an almost surely finite number of iterations. Numerical experiments illustrate the error decay of the VS approximations, as well as finite-step recovery in the parameter-independent case.

math.NA↗
arXiv2026

LoopGR: Parameter-Efficient Scaling for Generative Recommendation via Recurrent Depth

Generative recommendation formulates item recommendation as autoregressive generation rather than a discriminative scoring problem. Although empirical studies exhibit favorable scaling behavior in recommendation, unleashing the benefits of scaling remains challenging under the strict model size and inference cost constraints of real-world systems. A central challenge is to combine expressive modeling of user-item interaction histories with flexible computational depth without proportionally increasing model size. We introduce LoopGR, a parameter-efficient scaling framework for generative recommendation via recurrent depth. LoopGR employs a tailored hierarchical recurrent depth mechanism that alternates between a shared generative decoder and a lightweight context updater. For efficient inference, we further develop recurrent trajectory self-distillation that distills a low-budget student from a high-budget teacher by aligning both predictive distributions and evolution directions. Extensive experiments show that LoopGR consistently outperforms strong generative recommendation baselines. Compared with a representative encoder-decoder baseline, LoopGR achieves superior performance while reducing model size by up to 52%. These results establish recurrent depth as a new scaling dimension, offering a flexible and resource-efficient scaling path for generative recommendation.

cs.IR↗
arXiv2026

Search for Ultra-Relativistic Magnetic Monopoles with LHAASO-KM2A

Ultra-relativistic magnetic monopoles traversing the atmosphere are expected to initiate extensive air showers characterized by a muon deficit and a compact lateral profile. Using 1438.81 live days of data from the LHAASO-KM2A array, we present a search for these cosmic particles within the Lorentz factor range of $10^5$ to $10^7$. A machine-learning classifier utilizing lateral shower shape observables from the electromagnetic detectors is deployed to separate the signal from the hadronic background. No statistical excess above the background expectation is observed, and upper limits at the 90\% confidence level are established on the isotropic monopole flux. The resulting upper limit is $2.6 \times 10^{-16}\,\mathrm{cm}^{-2}\,\mathrm{s}^{-1}\,\mathrm{sr}^{-1}$ at $γ= 10^5$ and reaches $8.8 \times 10^{-19}\,\mathrm{cm}^{-2}\,\mathrm{s}^{-1}\,\mathrm{sr}^{-1}$ at $γ= 10^7$, which improves upon the astrophysical Parker bound by over three orders of magnitude. This work establishes constraints on the monopole flux within a previously unconstrained velocity window.

astro-ph.HE↗
arXiv2026

Local Allow, Global Deny: Why Per-Action Authorization Does Not Compose in LLM Agents

Autonomous LLM agents act through tools under per-action authorization: a local guard screens each tool call in isolation. We show that local authorization does not compose into global authorization. A compositional authorization failure (CAF) is a locally authorized execution, or a pair of locally authorized executions, that violates a global policy spanning intent, purpose, provenance, consent, and delegation. We characterize CAFs along two axes, expressiveness (single-trace safety versus relational k-safety) and observability (a ladder of monitor profiles). Our central result is an impossibility: relational purpose violations form a 2-safety hyperproperty with no per-trace locus of blame, so a per-action monitor that never raises a false alarm detects none of them, independent of the underlying model or decision procedure; they become visible only under cross-execution monitoring, which none of the six guardrails we survey implements. We make this constructive with AL-SYNTH, a SAT/SMT synthesizer (using no language model) that emits minimal CAF witnesses, each checked against an exhaustive oracle. The empirical signature is sharp: across nine LLM judges the per-action view approves the synthesized witness almost without exception, blocking it at a rate comparable to a benign control, yet the same models flag it when shown the two executions together. Per-action authorization is therefore structurally insufficient for relational policy conjuncts not reducible to single-trace safety: to bound them, a monitor must compare executions or accept false alarms.

cs.CR↗
arXiv2026

ACE-Compression: An Ability-Centric Evaluation of Compressed Large Language Models

Model compression makes large language models deployable, but today's evaluation tells an incomplete story. Standard practice measures retained task performance and stops there. This misses a critical fact: compression perturbs the distributed knowledge embedded across billions of weights, and task scores alone cannot reveal which underlying abilities survive and which quietly collapse. To solve this question, we introduce ACE-Compression, an ability-centric evaluation framework. Instead of asking only "does the compressed model still answer correctly?", we ask "which of its core abilities remain intact?" Our framework probes four deployment-facing dimensions: procedural reasoning and constraint execution, knowledge grounding and truthfulness, social cognition and affective understanding, and persona fidelity and stylistic consistency. Across more than 35 compressed checkpoints from six open-weight LLMs-covering quantization and pruning-we find that compression redistributes ability profiles. Coarse, local, and surface-level signals often persist, while fine-grained, long-tail, and context-dependent abilities become fragile. Quantization consistently outperforms pruning in retention, but the practical meaning of that gap depends entirely on which ability an application requires. Based on our findings, deployers can choose compression methods by the ability profile their application needs, not just by a leaderboard average. And algorithm designers gain diagnostic signals to build compression methods that preserve what matters. We believe that ACE-Compression turns compression from a black-box accuracy drop into an interpretable intervention-one we can measure, understand, and ultimately improve.

cs.AI↗
arXiv2026

On the Nonoccurrence of Gradient Restart in Nesterov Accelerated Gradient Method

Gradient restarting often improves the practical performance of accelerated gradient methods for unconstrained convex optimization problems, but a general theoretical guarantee of such improvement remains unclear. In this paper, we construct smooth, strongly convex objectives with Lipschitz-continuous gradients and corresponding initial points for which gradient restart never occurs, both for Nesterov's accelerated gradient method and for its ordinary differential equation model. From these examples, we conclude that strong convexity and smoothness alone do not guarantee that gradient restart improves the convergence behavior of the accelerated method or its continuous-time model. In particular, for the continuous-time example, the objective gap is asymptotic to $1/(πt^3)$. We therefore obtain a negative answer to the open problem raised by Su, Boyd, and Cand{è}s [\textit{J. Mach. Learn. Res.}, 2016, 17(153), 1--43] of whether gradient restart guarantees linear convergence of the continuous dynamics for smooth strongly convex objectives.

math.OC↗
arXiv2026

BridgeAfford: Bridging Query-Target Gap for Generalizable 3D Affordance Grounding

Language-guided 3D affordance grounding aims to localize functional regions on 3D objects according to interaction intents. However, existing methods exhibit poor generalization to unseen objects. One challenge is the query-target gap: a semantic discrepancy between query features encoding interaction intents and target-region features, leading to inaccurate grounding. End-to-end training favors query representations tailored to seen objects, limiting their compatibility with shifted target-region features on unseen objects. To address this issue, we propose BridgeAfford, a framework that reduces the query-target semantic gap from two complementary perspectives. First, Interaction Region Alignment (IRA) promotes cross-object alignment among geometrically similar interaction regions during training. This enables structures on unseen objects that resemble previously observed ones to produce representations similar to those learned during training, facilitating target-region recognition. Second, Visual Evidence Aggregation (VEA) leverages a 2D affordance model to refine the attention distribution and guide the query toward the correct region, yielding more target-aware queries. Extensive experiments on the LASO and PIAD datasets demonstrate that BridgeAfford outperforms existing methods, particularly on unseen objects, highlighting its robust generalization ability.

cs.CV↗
arXiv2026

DoubtLess: Training-Free Contrastive Decoding for Efficient Reasoning

Reasoning models achieve strong performance through long chains of thought (CoT), but often spend more tokens than needed to solve a problem. Existing ways to shorten the CoT require additional training for each model, add a fixed steering vector to the model's hidden states, or use decoding heuristics such as stopping early or banning words like "wait", which can reduce accuracy. We propose DoubtLess, a training-free decoding method in which a much smaller reasoning model steers the large one towards brevity. The small model reads the large model's CoT so far twice, once prompted to be brief (the brief thinker) and once prompted to be verbose (the verbose thinker), and the difference between its two logit vectors, scaled by a strength, is added to the large model's logits at every step of the CoT. On seven benchmarks of competition mathematics, graduate-level science and competitive programming, DoubtLess shortens the CoT of Qwen3-32B by 31% with unchanged accuracy, outperforming five inference-time baselines. We also show that DoubtLess reduces thinking tokens per turn by 20% in a self-debugging coding agent with unchanged task performance. It also generalizes to two other model families with unchanged accuracy. When combined with training-based compression, it further shortens the CoT of a model already trained for brevity. Ablations and behavioral analysis show that DoubtLess reduces repeated checking, with most token savings coming after the correct answer first appears in the CoT. Our code is available at https://github.com/leoluolol/DoubtLess.

cs.CL↗
arXiv2026

CivWorldBench: Evaluating Language World Models in Structured Multi-Agent Sandbox Worlds

The growing use of language models for forecasting motivates a broader evaluation of their world-model capabilities. Such evaluation must go beyond predictive accuracy to assess hidden-state inference and intervention prediction. Real-world forecasting benchmarks offer limited access to hidden-state ground truth and controlled experiments. We introduce CivWorldBench, a benchmark built on a multi-agent strategy game engine with recorded internal states, controlled observations, and paired intervention rollouts. Its 1,900 decision points from 475 worlds support three task levels: association forecasting, belief inference, and intervention prediction. Reference predictors support attributable evaluation of capability gaps, with an expert harness and empirical ceiling estimates contextualizing scores. Stored trajectories make intermediate predictions verifiable, while agent-play experiments test whether world-model capabilities support decision-making. The main panel comprises eleven frontier language models. Composite benchmark scores correlate with real-world forecasting leaderboards on matched model panels, more closely than with a general-capability index, supporting its use as a proxy for real-world forecasting capability. Strong association forecasts do not consistently extend to belief inference or intervention prediction, and effect magnitudes remain poorly calibrated despite informative direction predictions. Process verification reveals errors missed by final-answer evaluation, and structured reasoning improves intervention prediction for the strongest model in the process study. Agent-play experiments identify useful assisted plans and motivate evaluating decision-relevant capabilities beyond association forecasting. These findings identify hidden-state inference, intermediate-state prediction, and effect calibration as priorities for world-model training.

cs.CL↗
arXiv2026

Structure-Aware Numerical Stabilization for Half-Precision Proximal Policy Optimization

Local online training on resource-constrained platforms requires low computational and training-state overhead. We study the numerical stability and computational efficiency of half-precision proximal policy optimization (PPO) on commodity GPUs. Directly converting PPO training to FP16 is unreliable because numerical errors feed back through policy updates and subsequent data collection, while PPO repeatedly compares a current policy with a fixed behavior policy. We propose PPO-FP16 with three structure-aware modules. Global scalar protection stabilizes statistics shared across a minibatch or parameter update. Policy-change residual protection extracts and preserves small differences between the current and behavior policies. Efficient persistent-update protection stabilizes optimizer state and parameter writeback while reducing state storage. These changes preserve the PPO objective, hyperparameters, and training budget. Across six continuous-control tasks and five seeds, PPO-FP16 achieves the lowest mean return-AUC rank (1.90) among the four methods and ranks first in 17 of 30 environment-seed comparisons. Eight of nine performance configurations yield PPO update speedups ranging from 1.16 to 2.03 times. Relative to Full FP32 Adam, optimizer-state memory is reduced by 71.7 to 74.9 percent, and peak allocated GPU memory is reduced by up to 44.5 percent. The results show that protecting a small set of structurally important quantities can deliver stable, efficient FP16 PPO without retuning.

cs.LG↗
arXiv2026

Counterexamples and gap phenomena for derived delooping levels

Guo and Igusa introduced the derived and sub-derived delooping levels as refinements of the delooping level and asked how these two invariants compare, whether the derived delooping level always coincides with the opposite big finitistic dimension, and, if not, how large their difference can be. We answer these questions. First, we prove that every Artin algebra $A$ satisfies $\operatorname{ddell} A\leq \operatorname{sub-ddell} A$. This gives a positive answer to the ordinary-degree comparison problem, whereas a single explicit eight-dimensional algebra $C$ satisfies $k\text{-}\operatorname{sub-ddell} C=0<1=k\text{-}\operatorname{ddell} C$ for $k\geq 2$. We then compute, for every $k\geq 1$, all $k$-derived and $k$-sub-derived delooping levels of the monomial family of Barrios--Lanzilotta--Mata. In particular, that family satisfies $k\text{-}\operatorname{ddell} A_{n,s}=\operatorname{Findim}(A_{n,s}^{\mathrm{op}})$ for $k\geq 1$. In ordinary degree, this recovers an equality previously announced by Lam. We also construct a two-vertex, eleven-dimensional monomial algebra $Λ$, over an arbitrary field, such that $\operatorname{Findim}(Λ^{\mathrm{op}})=1<2=\operatorname{ddell}Λ=\operatorname{dell}Λ$. The same strict inequality is obtained by Gao, Liu and Xu using different algebras. A ten-dimensional non-monomial variant with a commutative corner algebra is also given. Finally, we construct a family of finite-dimensional monomial algebras $B_r$ satisfying $\operatorname{Findim}(B_r^{\mathrm{op}})=1$ and $\operatorname{ddell} B_r=2r+1$. Thus the difference $\operatorname{ddell} A-\operatorname{Findim}(A^{\mathrm{op}})$ is unbounded even among monomial algebras with fixed opposite big finitistic dimension.

math.RT↗
arXiv2026

Big Finitistic Dimensions of Radical-Cube-Zero Algebras

We construct, over any field, a ten-dimensional algebra $A$ with $(\operatorname{rad}A)^3=0$ such that $\operatorname{findim}A=2$, $\operatorname{Findim}A=\infty$, and $\operatorname{Findim}(A^{\mathrm{op}})=0$. An explicit inverse-syzygy construction gives countably generated right modules of every positive finite projective dimension, disproving the big finitistic dimension conjecture. At each step we identify the full projective-cover kernel, and nonzero Ext detects the exact resolution length. For this algebra, we classify the modules of projective dimension at most one that embed in radicals of projective modules, with no generation restriction, in terms of invertible linear operators. We determine all dimension vectors of finite-dimensional modules of projective dimension two and classify those attaining the sharp dimension bounds, including all fourteen-dimensional modules of projective dimension two. For arbitrary Artin algebras, we give recursive sufficient criteria that control finite projective resolutions through radical-annihilated ideals and suitable corners. For finite-dimensional elementary radical-cube-zero algebras, we prove finiteness of the big finitistic dimension in the two-vertex case with one loop at each vertex and obtain multiplication-map criteria for bounds of one or two. Finally, an exact representation embedding yields wildness of the projective-dimension-two class and, over every field, a superdecomposable pure-injective module in that class.

math.RT↗
arXiv2026

Certified Multi-Source Integrity for Structured Agent Actions

LLM agents increasingly take privileged, often irreversible structured actions, such as paying an invoice. They assemble each action from action-critical fields in documents and tool outputs that an adversary can corrupt, and indirect prompt injection can drive the model itself to extract attacker-chosen values. Current defenses gate on a source's trust label or certify free-text answer quality. None certifies the integrity of a coupled, policy-bound structured action under a corruption budget that accounts for shared upstream sources. We characterize when such an action is safely certifiable and give the maximally live safe certifier. It admits an action only when each field clears the rule its evidence structure supports: a bounded corruption radius over corruption-distinct evidence classes, counted by a minimum hitting set so that re-publishing or laundered copies cannot manufacture a quorum, deterministic reconciliation for complementary fields, and a trusted anchor where the evidence leaves a field single-sourced. We formalize two robustness notions, validate each mechanism by ablation, and measure how often the multi-source precondition holds on sanctions designations (70,966 entities) and software supply-chain provenance (450 packages). Under upper-bound proxies, genuine corroboration is a minority phenomenon in both, and naive attestation counting overstates it, since witnesses that look independent collapse to two corruption-distinct domains once shared origin is counted. Across five current models in a real agent loop, a realistic injection fools every model but one and a naive agent then executes the fraudulent action on most attacks. The certifier admits no unsafe action and recovers the correct value where corroboration permits, while action-gating and provenance baselines are broken in every world of our harness by some attack in its space.

cs.CR↗