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Zihao Li

Publications and source records attributed to Zihao Li.

At least 55 records · Page 3Linked to original sources
arXiv2026

Selling Consumer Data under Limited Commitment

A data broker repeatedly negotiates with a producer who has privately known production costs and values consumer data for price discrimination. The broker cannot commit to future offers. When he can offer rich menus of dataset--price pairs, the unique equilibrium outcome immediately implements the commitment-optimal mechanism, so limited commitment is irrelevant. When he can offer only a single dataset--price pair at a time, by contrast, we obtain a folk theorem. There is always an equilibrium in which the market clears immediately at a low price, while, when the parties are sufficiently patient, any payoff between this benchmark and the commitment payoff can be sustained. This multiplicity rests on a property specific to data: free disposal by the producer and zero marginal cost of provision make the efficient dataset for a given type nonunique, leaving the broker credible flexibility over future offers. Equilibrium multiplicity is therefore an economic prediction of the model, with identical fundamentals supporting persistently different negotiated outcomes.

econ.TH↗
arXiv2026

TriPilot-FF: Coordinated Whole-Body Teleoperation with Force Feedback

Mobile manipulators broaden the operational envelope for robot manipulation. However, the whole-body teleoperation of such robots remains a problem: operators must coordinate a wheeled base and two arms while reasoning about obstacles and contact. Existing interfaces are predominantly hand-centric (e.g., VR controllers and joysticks), leaving foot-operated channels underexplored for continuous base control. We present TriPilot-FF, an open-source whole-body teleoperation system for a custom bimanual mobile manipulator that introduces a foot-operated pedal with lidar-driven pedal haptics, coupled with upper-body bimanual leader-follower teleoperation. Using only a low-cost base-mounted lidar, TriPilot-FF renders a resistive pedal cue from proximity-to-obstacle signals in the commanded direction, shaping operator commands toward collision-averse behaviour without an explicit collision-avoidance controller. The system also supports arm-side force reflection for contact awareness and provides real-time force and visual guidance of bimanual manipulability to prompt mobile base repositioning, thereby improving reach. We demonstrate the capability of TriPilot-FF to effectively ``co-pilot'' the human operator over long time-horizons and tasks requiring precise mobile base movement and coordination. Finally, we incorporate teleoperation feedback signals into an Action Chunking with Transformers (ACT) policy and demonstrate improved performance when the additional information is available. We release the pedal device design, full software stack, and conduct extensive real-world evaluations on a bimanual wheeled platform. The project page of TriPilot-FF is http://bit.ly/46H3ZJT.

cs.RO↗
arXiv2026

TRACE: Trajectory-Routed Causal Memory for Delayed-Evidence Visuomotor Imitation

Robots under autonomous operation may require decisions based on evidence that is no longer visible. We study delayed-evidence tasks, where an early cue disappears before a later decision point, so visually similar observations can require different actions. In these settings, the current observation is not a sufficient state for control. We introduce TRAjectory-routed Causal Evidence (TRACE), a memory framework for visuomotor imitation policies. TRACE stores task-relevant visual and robot-state evidence, such as object identity, target choice, or route-dependent state, in a fixed-size latent memory that remains bounded over long episodes. Instead of indexing memory by raw time or manually provided task labels, TRACE uses path signatures: compact, order-sensitive features of the executed robot-state trajectory. These signatures do not store the visual cue itself; rather, they provide trajectory-conditioned keys for writing and retrieving the evidence stored when the cue was visible. When the robot later reaches an ambiguous observation, the policy conditions on TRACE memory to recover the missing context and choose the correct branch. TRACE attaches through lightweight adapters to policies, without changing the policy backbone, action head, or imitation objective. Across real-world long-horizon manipulation tasks with visually ambiguous branch points, TRACE improves branch selection and task success over alternative baselines, including short-history and recurrent memory. Project page: https://jeong-zju.github.io/trace

cs.RO↗
arXiv2026

Robots that Collaborate: Sequential Asymmetric Imitation for Learning Coupled Robot Policies

Collaborative mobile manipulation requires robots to coordinate with a partially observed partner while physically interacting through shared objects. This is difficult because failures often arise not from poor local skills, but from mistimed waiting, yielding, pulling, releasing, or repositioning. We study this problem with two bimanual mobile manipulators coupled through rigid and deformable objects. We propose Sequential Asymmetric Imitation (SAI), a single-teleoperator curriculum for learning coupled multi-robot behaviors without synchronized dual-operator demonstrations or explicit inter-robot communication. SAI trains Robot A from unilateral demonstrations with a compliant human partner, trains Robot B against the deployed Robot A policy, and then refines Robot A using sparse interventions near coordination failures. This staged process exposes the policies to increasingly realistic partner behaviors, including delay, phase mismatch,insufficient yielding, and interaction conflict. Across real-world dual-robot manipulation tasks, SAI improves task success, phase synchronization, and partner-contingent yielding over independent imitation and curriculum-ablation baselines. These results suggest that physically coupled collaboration can be learned through the structure of the imitation curriculum, rather than through synchronized multi-operator demonstrations or explicit coordination mechanisms. More videos on project page:http://cyc0429.github.io/sai-project-page/

cs.RO↗
arXiv2026

Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach

Causal discovery seeks to uncover causal relations from data, typically represented as causal graphs, and is essential for predicting the effects of interventions. While expert knowledge is required to construct principled causal graphs, many statistical methods have been proposed to leverage observational data with varying formal guarantees. Causal Assumption-based Argumentation (ABA) is a framework that uses symbolic reasoning to ensure correspondence between input constraints and output graphs, while offering a principled way to combine data and expertise. We explore the use of large language models (LLMs) as imperfect experts for Causal ABA, eliciting semantic structural priors from variable names and descriptions and integrating them with conditional-independence evidence. Experiments on standard benchmarks and semantically grounded synthetic graphs demonstrate state-of-the-art performance, and we additionally introduce an evaluation protocol to mitigate memorisation bias when assessing LLMs for causal discovery.

cs.AI↗
arXiv2026

A Statistical Framework for Auditing Behavioral Dependence and Induced Bias in LLM Judges

The rapid growth of the large language model (LLM) ecosystem raises a critical question: are seemingly diverse models truly independent? Shared pretraining data, distillation, and alignment pipelines can induce hidden behavioral dependencies, or latent entanglement, that undermine multi-model systems such as LLM-as-a-judge pipelines and ensemble verification, which implicitly assume independent signals. In practice, this manifests as correlated reasoning patterns and synchronized failures, where apparent agreement reflects shared error modes rather than independent validation. To address this, we develop a statistical framework for auditing behavioral entanglement among black-box LLMs. Our approach introduces a multi-resolution hierarchy that characterizes the joint failure manifold through two information-theoretic metrics: (i) a Difficulty-Weighted Behavioral Entanglement Index (BEI), which amplifies synchronized failures on easy tasks, and (ii) a Cumulative Information Gain (CIG) metric, which captures directional alignment in erroneous responses. Through experiments on 18 LLMs from six model families, we identify statistically significant behavioral entanglement. Such behavioral dependence is associated with judge over-endorsement bias on a disjoint MMLU-Pro evaluation set (rho = 0.508 for BEI and rho = 0.520 for CIG; p < 0.01). The association further transfers to the MATH-500 benchmark (rho = 0.441 for BEI and rho = 0.457 for CIG; p < 0.05), providing cross-benchmark evidence that the identified dependency structure generalizes beyond the data and response format used for its estimation. Finally, we demonstrate a practical use case of entanglement through de-entangled verifier ensemble reweighting, achieving 3.5 and 2.6 percentage-point gains in accuracy and precision, respectively, over majority voting.

cs.AI↗
arXiv2026

Dynamic Interaction-Aware and Causality-Disentangled Framework for Multimodal Sentiment Analysis

Although Multimodal Sentiment Analysis (MSA) effectively leverages rich information from language, visual, and acoustic modalities, existing methods still face two core challenges: 1) static conflict suppression mechanisms fail to adapt to dynamic variations across samples, and 2) the inherent sentimental bias within the language modality, which can misguide learning from other modalities, remains entangled. To this end, we propose a Dynamic Multimodal Causal Disentanglement and Adaptive Fusion Framework (MCAF). Its cornerstone is the Multi-Granularity Causal Dynamic Router and a Conditional Diffusion Denoising Module. First, we introduce a causal intervention module based on the information bottleneck principle, which builds a Structural Causal Model to disentangle sentimental bias from language features, yielding a "de-confounded" language representation as a pure guiding signal. Second, we devise a Dynamic Multimodal Router that evaluates the interaction states (complementary, conflicting, or redundant) among visual, acoustic, and de-confounded language signals in real-time across three levels: feature, temporal, and modality, then adaptively allocates weights and routes information flow for fine-grained regulation. Finally, a lightweight Conditional Diffusion Denoising Module performs iterative denoising on the fused joint representation to explicitly filter out residual irrelevant information, generating a robust hyper-modality representation. Extensive experiments on the CMU-MOSI and CMU-MOSEI benchmarks show that MCAF sets new state-of-the-art on key classification metrics, achieving an Acc-2/F1 of 86.52%/86.51% on MOSI and 86.72%/86.65% on MOSEI, while remaining highly competitive on others. Comprehensive analyses and visualizations further validate its efficacy in dynamically perceiving interactions, disentangling bias, and enhancing interpretability.

cs.MM↗
arXiv2026

Parameter Efficient Machine Unlearning on Hybrid Resistive Memory based Compute-in-Memory Accelerators

Resistive memory compute-in-memory accelerators provide energy efficient analogue matrix vector multiplication for neural network inference, but frequent reprogramming of analogue weights remains costly because of device variability and iterative write and verify operations. This limitation hinders their use in edge model adaptation, including approximate machine unlearning and continual learning, where model parameters may need to be updated repeatedly in response to data deletion requests or newly arriving tasks. Here we present a co-design approach across hardware and software that maps frozen pretrained weights to analogue resistive memory arrays while placing trainable low rank adaptation branches in SRAM connected digital compute. By using LoRA style parameter efficient updates, the proposed scheme confines adaptation to a small set of digital parameters and avoids repeated reprogramming of the analogue backbone. To our knowledge, this work provides the first experimental demonstration of approximate machine unlearning on a fabricated resistive memory CIM accelerator. We validate the framework on a 180 nm 128x128 1T1R resistive-memory macro for face recognition, and through circuit-accurate simulations for speaker authentication and stylized image generation tasks, owing to the substantial model sizes involved. Compared with a baseline that directly updates analog weights, our hybrid mapping reduces analog training/update cost by up to 148x, on-chip deployment overhead by up to 388x, and inference energy by up to 59x, while preserving competitive task performance. These results show that hybrid analogue-digital LoRA mapping can enable efficient post-deployment adaptation on RM-CIM hardware, although formal machine-unlearning guarantees and large-scale system integration remain open challenges.

cs.ET↗
arXiv2026

A Physics-Grounded Benchmark for Multi-Agent Dynamics in World Models

Generative world models hold immense promise as scalable simulators for autonomous systems, particularly for synthesizing rare but safety-critical multi-agent interactions, such as vehicle collisions. However, current evaluation paradigms index heavily on visual fidelity and semantic alignment, leaving a critical blind spot: they cannot reliably quantify whether generated dynamics actually obey the fundamental physical laws required for reliable simulation. Assessing this physical plausibility is inherently difficult due to a lack of physical metrics and the challenge of extracting metric-scale kinematics from uncalibrated video rollouts. To bridge this gap, we introduce CrashTwin, a physics-grounded evaluation framework designed to stress-test the physical trustworthiness of world models. CrashTwin couples a diverse dataset of multi-agent collision scenarios, comprising 25K controllable synthetic and 12K in-the-wild real-world collision sequences with a novel calibration-free reconstruction pipeline, enabling the recovery of 3D physical attributes directly from world model rollouts. We propose a diagnostic suite that systematically evaluates three dimensions: spatio-temporal consistency, momentum and kinetic energy conservation, and world-dynamics integrity. Extensive benchmarking of state-of-the-art models reveals a crucial insight: high perceptual quality frequently masks severe physical violations during complex interactions. By quantitatively exposing these failure modes, CrashTwin provides a vital diagnostic tool for developing physically grounded world models capable of reliable real-world simulation.

cs.CV↗
arXiv2026

Integrated Automated Car Following and Lane-changing control based on a Parametrized Deep Q-network with Hybrid Action Space

Lane-change, a triggering of traffic disturbances to the upstream vehicles, is detrimental to traffic safety and efficiency. Coupled with car-following behavior, the joint maneuvers depict the general picture of how traffic disturbances generate and propagate through vehicle streams, especially under traffic congestion. This study proposes an integrated control framework for lane-changing and car-following for connected and automated vehicles (CAVs), where those two tasks are largely treated as independent driving tasks by prevailing methods. Utilizing the Parametrized Deep Q-Network (P-DQN) with a hybrid action space, the framework adeptly models multiple objectives in CAV control. The P-DQN's high-level control is employed for discrete lane-change decisions, while its low-level control manages continuous acceleration actions, i.e., lateral and longitudinal acceleration. These actions are interdependently determined, seamlessly integrating car-following and lane-changing control. By training to maximize cumulative rewards, the proposed control strategy ensures driving safety as well as the efficiency of car-following, lane-changing, and lane-keeping. Through numerical experiments, it is indicated that the P-DQN outperforms separated control methods, e.g., the combination of the Minimizing Overall Braking Decelerations Induced by Lane Changes (MOBIL) model and the Intelligent Driver Model (IDM), in terms of safety and comfort.

eess.SY↗
arXiv2026

A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges

Agents4Pentest, an emerging class of LLM-based autonomous penetration testing systems, has become a rapidly growing area in security research. Despite this growth, the field still lacks a unified taxonomy, a systematic understanding of how agent architectures and evaluation benchmarks have co-evolved, and a clear characterization of remaining capability and reliability gaps. This survey addresses these gaps through a systematic analysis of 81 papers between 2023 and 2026. We organize the literature into six categories: evaluation benchmarks, general-purpose systems, domain-specific frameworks, CTF-based systems, defense-oriented research, and surveys. We further trace a four-phase architectural evolution from text-only reasoning agents to agents trained with Reinforcement Learning with Verifiable Rewards (RLVR), showing that each transition is driven by a distinct capability bottleneck. Our analysis yields several key findings. First, RLVR marks a shift in capability acquisition from imitation of expert demonstrations to reward-driven self-improvement, enabling agents to discover previously undocumented attack strategies. Second, CTF platforms have evolved from evaluation testbeds into dual-purpose infrastructure for both agent evaluation and RL training. Third, domain-specific frameworks improve efficiency through recurring specialization mechanisms, but their gains remain largely confined to narrow task classes and are difficult to compare across domains because existing evaluations rely on different benchmarks. Fourth, the field is expanding beyond offensive automation toward adversarial defense and security compliance. Across these categories, we identify three structurally linked open challenges: evaluation reliability, limited performance on multi-stage attack scenarios, and scarcity of high-quality training data.

cs.SE↗
arXiv2026

JWST COSMOS-3D: Spectroscopic Census and Luminosity Function of [O III] Emitters at 6.75<z<9.05 in COSMOS

We present a catalogue of spectroscopically selected [OIII]+Hb emitters at 6.75<z<9.05 and the resulting [OIII] 5008 ÅLuminosity Function (LF) in the COSMOS field. We leverage the 0.33 deg$^2$ covered by COSMOS-3D using NIRCam/WFSS F444W to perform the largest spectroscopic search for [OIII] emitters at 6.75<z<9.05. We present our catalogue of 249 [OIII] emitters and their associated completeness function. The inferred constraints on the [OIII] LF enable us to characterise the knee of the [OIII] LF, resulting in improved [OIII] LF constraints at z~7,8. Notably, we find evidence for a decline of the [OIII] luminosity density between z~7 and z~8, which could be expected if the metallicity of [OIII] emitters, as well as the cosmic star-formation rate density, is declining at these redshifts. We find that theoretical models that reproduce the z~7,8 [OIII] LF do not reproduce well the [OIII] equivalent width distribution, pointing to potential challenges in the modelling of[OIII] and other nebular lines in the early Universe. Finally, we provide the first constraints on the cosmic variance of [OIII] emitters, estimating at 15% the fractional uncertainty for the z~7,8 [OIII] LF in the 0.33 deg$^2$ field. This estimate is in good agreement with that inferred from clustering, and shows that the [OIII] LF derived from smaller extragalactic legacy fields is strongly affected by cosmic variance. Our results highlight the fundamental role that wide-area JWST slitless surveys play to map the galaxy large-scale structure down into the reionisation era, serving as a springboard for a variety of science cases.

astro-ph.GA↗
arXiv2026

ASPIRE: The Environments and Dark Matter Halos of Luminous Quasars in the Epoch of Reionization

We present a systematic study of the environments of 25 luminous quasars at $z > 6.5$ from the ASPIRE program. Using JWST/NIRCam WFSS data, we identified 487 galaxies at $5.3 \lesssim z \lesssim 7.0$ exhibiting [OIII] emission. Among these, 122 [OIII] emitters lie within $|Δv_{\rm los}| < 1000~{\rm km~s^{-1}}$ of the quasars, corresponding to a $\sim9.4$-fold enhancement relative to the average galaxy density at other redshifts. Furthermore, we identified 16 [CII]-emitting galaxies at the quasar redshifts from ALMA mosaic observations. A cross-correlation function (CCF) analysis between quasars and [OIII]+[CII] emitters yields a cross-correlation length of $r_0^{\rm QG} = 8.68^{+0.51}_{-0.55}~h^{-1}~\mathrm{cMpc}$ and a auto-correlation of $r_0^{\rm{QQ}}=15.76^{+2.48}_{-2.70}~h^{-1}~{\rm cMpc}$, indicating that $z \sim 7$ quasars reside in dark matter halos with $M_{\rm halo} = 10^{12.27^{+0.21}_{-0.26}}~M_\odot$. Notably, the number of [OIII]-emitting galaxies at quasar redshifts varies significantly from field to field, ranging from zero to twenty, highlighting a diverse quasar environment. Remarkably, seven quasars trace significant galaxy overdensities (i.e., protoclusters), with $δ_{\rm gal} > 5$ within a volume of $V \sim 500~{\rm cMpc^3}$. We also find that $|Δv_{\rm los}|$ increases rapidly toward smaller galaxy-quasar separations in protocluster fields, consistent with galaxy kinematics around extremely massive halos in cosmological simulations. By combining JWST and ALMA data, we reveal the complex and diverse environments of these early quasars, providing robust evidence that the earliest luminous quasars are effective tracers of galaxy overdensities, albeit with substantial field-to-field variation.

astro-ph.GA↗
arXiv2026

TSAQA: Time Series Analysis Question And Answering Benchmark

Time series data are integral to critical applications across domains such as finance, healthcare, transportation, and environmental science. While recent work has begun to explore multi-task time series question answering (QA), current benchmarks remain limited to forecasting and anomaly detection tasks. We introduce TSAQA, a novel unified benchmark designed to broaden task coverage and evaluate diverse temporal analysis capabilities. TSAQA integrates six diverse tasks under a single framework ranging from conventional analysis, including anomaly detection and classification, to advanced analysis, such as characterization, comparison, data transformation, and temporal relationship analysis. Spanning 210k samples across 13 domains, the dataset employs diverse formats, including true-or-false (TF), multiple-choice (MC), and a novel puzzling (PZ), to comprehensively assess time series analysis. Zero-shot evaluation demonstrates that these tasks are challenging for current Large Language Models (LLMs): the best-performing commercial LLM, Gemini-2.5-Flash, achieves an average score of only 65.08. Although instruction tuning boosts open-source performance: the best-performing open-source model, LLaMA-3.1-8B, shows significant room for improvement, highlighting the complexity of temporal analysis for LLMs.

cs.AI↗
arXiv2026

Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement

We introduce Goedel-Architect, an agentic framework for formal theorem proving in Lean 4 centered on blueprint generation and refinement. A blueprint is a dependency graph of definitions and lemmas that builds up to the main theorem. First, Goedel-Architect generates a blueprint of formally stated definitions and lemmas, along with declared dependencies. This blueprint is optionally guided by a natural language proof. Then, a tool-equipped Lean prover component closes each open lemma node in parallel using relevant dependencies. Failed lemmas in turn drive refinement of the global blueprint. This strategy contrasts with other mainstream approaches which use recursive lemma decomposition, and can inefficiently loop on dead-end strategies. Using the open-weight DeepSeek-V4-Flash (284B-A13B) as the backbone, Goedel-Architect attains 99.2% pass@1 on MiniF2F-test and 75.6% pass@1 on PutnamBench. With an optional natural-language proof seeding the initial blueprint on the harder problems, we additionally close the remaining two MiniF2F-test problems (reaching 100%), lift PutnamBench to 88.8% (597/672), and solve 4/6 on IMO 2025, 11/12 on Putnam 2025, and 3/6 on USAMO 2026. This represents state-of-the-art performance for an open-source pipeline at a price point up to 500x less than comparable open-source pipelines.

cs.AI↗
arXiv2026

Harnessing Generalist Agents for Contextualized Time Series

Time series are often embedded in rich contexts that are essential for holistic modeling. Moreover, real-world practitioners often require end-to-end workflows for analyzing temporal dynamics, where widely studied tasks such as forecasting are only one step in a broader solution loop. While generalist AI agents offer a promising interface for such workflows under complex contexts, they still operate primarily in textual spaces that are not fully aligned with structured temporal signals. In this work, we introduce TimeClaw, an agentic harness framework for time series that equips generalist LLM agents with the time series-native runtime support needed for contextualized temporal reasoning. TimeClaw integrates executable temporal tools for grounded and auditable analysis, experience-driven capability evolution for creating reusable analytical routines, and episodic multimodal memory for retrieving relevant reasoning traces. Together, these components unlock harnessed open-ended temporal reasoning with contextual information. Extensive evaluation on multiple benchmarks covering diverse tasks across energy, finance, weather, traffic, and other real-world domains demonstrates improved performance of TimeClaw. Code is available at https://github.com/iDEA-iSAIL-Lab-UIUC/TimeClaw.

cs.AI↗
arXiv2026

Local Clustering on Complex Graphs and Complex Hypergraphs

Local/seeded clustering aims to find a compact cluster near the given starting instances. While most existing studies on graph clustering assume a discrete graph setting (i.e., unweighted, undirected graphs without self-loops), real-world graphs can be more complex. In this paper, we extend the classic non-approximating Andersen-Chung-Lang (ACL) clustering algorithm beyond discrete graphs and generalize its quadratic optimality to a wider range of complex graphs, including weighted, directed, and self-looped graphs and hypergraphs with edge-dependent vertex weights. Specifically, by leveraging PageRank, we propose two algorithms: GeneralACL for graphs and HyperACL for hypergraphs. We prove that, under two mild conditions, both algorithms can identify a quadratically optimal cluster in terms of conductance. Additionally, we provide experiments to validate our theoretical findings. Our code is available at https://github.com/iDEA-iSAIL-Lab-UIUC/HyperACL.

cs.SI↗
arXiv2026

Model-Based Quality Assessment for Massively Multilingual Parallel Data

Large-scale multilingual bitext often contains two distinct problems: non-parallel sentence pairs and low-quality translations. We decompose model-based assessment for such data into two independent components: parallelism assessment with multilingual embeddings and reference-free quality estimation (QE). For parallelism, we benchmark four embedding models on FLORES-200 and BOUQuET retrieval tasks, covering 6,654 source--target directions in our target language-pair inventory. For QE, we evaluate nine reference-free evaluators on professional FLORES-200 translations across 41,412 ordered source--target directions. Results show that no model is universally reliable across translation directions. Naive QE ensembles dilute strong model signals, while documented target-language coverage is strongly associated with higher QE scores. Overall, these findings suggest that multilingual parallel-data assessment is best approached as a direction-aware routing and calibration problem, where no single universal metric is expected to suffice across all languages.

cs.CL↗