arXiv subjects

Zihao Su

Publications and source records attributed to Zihao Su.

9 recordsLinked to original sources

arXiv2026

Visuo-tactile Diffusion Policy for Robotic Manipulation with Auxiliary Tactile Dynamics Prediction

Long-horizon robotic assembly requires reliable perception and action across grasping, alignment, and insertion. Vision captures global object pose and scene context, where as fingertip touch provides local contact cues that are especially useful as contact states change. We present a visuotactile diffusion policy that combines four-step histories from two camera views and two fingertip tactile images to condition action-sequence generation. A recurrent temporal representation integrates recent observations from both modalities. To encourage it to capture how contact evolves under action, an auxiliary objective predicts fingertip-image changes two steps ahead from the temporal representation and demonstrated actions. This objective is used only during training and adds no inference-time computation. Across three independent training seeds in MuJoCo assembly experiments, the full model achieves an average success rate of $59.0\% \pm 2.6\%$, improving on the visual-only baseline ($42.7\% \pm 1.5\%$) by 16.3 percentage points.

cs.RO↗

arXiv2026

Seeing Is No Longer Believing: Frontier Image Generation Models, Synthetic Visual Evidence, and Real-World Risk

Image generation systems can produce plausible photographs, readable documents, and consistent depictions of people and places. When these artifacts are presented as records of real events, they can influence decisions in news, finance, identity verification, medicine, and law. This narrative review examines selected public model documentation, incident reports, research, and governance sources available through 1 October 2026, with English and Chinese community material providing illustrative context. We distinguish vendor capability claims, documented incidents, experimental findings, and prospective harm pathways. The analysis connects realism, text rendering, reference consistency, editing, grounding, and production cost to the conditions under which synthetic images acquire evidentiary authority. Historical incidents illustrate these pathways; they do not establish misuse rates for current models. We compare provider restrictions, provenance systems, watermarking, platform labeling, and policy obligations, and explain how their functions differ from independent verification of a depicted event or transaction. The framework links artifact types and decision contexts to the functions of available controls. High-stakes decisions call for authenticated source records, corroboration through trusted channels, and proportionate review before action.

cs.CL↗

arXiv2026

Verbal tics in frontier language models: A critical review of current releases, research evidence, and public discussion

Repeated praise, canned reassurance, familiar contrasts, and conspicuous vocabulary are recurring subjects in discussions of large language models. Their interpretation depends on context: a conventional phrase may be useful, while a fluent answer may reinforce a false belief. This critical review examines linguistic habits and sycophancy across eight developer families: OpenAI, Anthropic, Google DeepMind, xAI, ByteDance, Moonshot AI, DeepSeek, and Xiaomi. We verify current public offerings against official release and API documentation, with an evidence cutoff of 1 October 2026. We synthesize research on lexical overrepresentation, stylistic variation, social warmth, and agreement, alongside benchmark methods and dated English and Chinese public discussions. The research reviewed documents recurring linguistic patterns and agreement that distorts judgment; comparable measurements of the newest releases are sparse in the retrieved set. Current user reports include both complaints and improved writing, with experiences varying by task and prompting. We propose separate measures of recurrence, contextual appropriateness, and belief distortion, with precise service records and language-specific annotation. This framework makes claims about writing quality and conversational reliability testable as model services change.

cs.CL↗

arXiv2026

From language-model stock rankings to testable economic rules: A computational audit

We test the stability, reproducibility and investment outcomes of language-model stock rankings. Four models and five numerical comparators share a portfolio engine over 72 monthly holding periods in the Shanghai Stock Exchange (SSE) 50, China Securities Index (CSI) 300 and CSI 500. Rankings use nine characteristics, and five repeated SSE 50 runs measure variation under identical inputs. Linear rules fitted to development-period model preferences are frozen before unseen-month, larger-pool and controlled-intervention tests. Their mean Spearman agreement with model rankings is 0.923-0.984 in the SSE 50 and 0.795-0.985 after transfer. Aggregate rank-change error falls relative to a zero-change prediction in twelve archived feature-group comparisons and eight matched single-feature comparisons, with Holm adjustments applied in separate nine-plus-three and six-plus-two families. Prediction of individual entries and exits remains weak (event Jaccard 0.000-0.125). Historical mean model compound annual growth rates range from 6.26% to 11.17%. At 10 basis points per side and six-month blocks, the twelve-comparison model-minus-rule return family and factor-controlled associations yield no adjusted finding. Three higher-cost, twelve-month-block comparisons favor a Terra rule within their twelve-test slices, with no adjusted finding across the full 144-test sensitivity grid. Two input-intervention batches totaling 5,184 responses supply paired intervention-return tests. The three-model batch has no bootstrap-adjusted finding at the primary block length; a Luna row-order effect appears under heteroskedasticity- and autocorrelation-consistent (HAC) adjustment within its three-test family but not in a pooled 24-test adjustment. Compact rules approximate aggregate rankings; return conclusions depend on comparison families, uncertainty methods and tie priorities.

cs.CE↗

arXiv2026

Mind the Gap: Rethinking I/O Design for Contact-Rich Visuomotor Policy Learning

Contact-rich teleoperation logs expose a policy I/O design choice: demonstrations may contain the robot execution (E), leader command (C), or both. These signals are not interchangeable: E2E may discard contact-generating command offsets, whereas E2C preserves these offsets but omits the robot's execution response. We propose Dual-State Conditioning (EC2C), which conditions on both E and C while predicting future C, exposing command-execution mismatch as a cue for contact, latency, payload, and operator compensation; in quasi-static contact, this cue is often force-correlated. On a low-cost setup without force, tactile, or motor-current policy input, EC2C outperforms E2E and a strong E2C baseline across several real-world contact-rich, force-sensitive, and dynamic tasks. These results support EC2C as a practical default I/O setting for contact-rich imitation learning. We further formulate latency-adaptive inpainting as a temporal extension of this I/O choice for action-chunking policies, and discuss when long histories help dynamic inference or introduce causal confounding.

cs.RO↗

arXiv2026

MEMOBench: A Process Level Memory Benchmark for Robotic Manipulation

Robotic manipulation often requires acting on information that is no longer visible, yet Vision-Language-Action policies are usually evaluated when the current observation largely determines the next action. Existing robotic memory benchmarks expose this gap, but they still rely mainly on final task success and therefore conflate forgetting with manipulation failure. We present \textbf{MEMOBench}, a benchmark for process level memory evaluation in robotic manipulation. MEMOBench includes 30 history dependent tasks, 1{,}500 expert demonstrations, and 4{,}200 executable checkpoint instances from 84 templates. Each checkpoint pairs coarse to fine language with a simulator predicate and labels one memory operation: Storage, Update, or Compression. These annotations define Memory Storage Rate, Memory Update Rate, and Memory Compression Rate, which measure memory fidelity alongside task success. Across standard and memory augmented VLA policies, the strongest memory module baseline reaches only 31.9\% average success rate, and high storage often coexists with weak update and compression. Checkpoint language also supervises semantic, contrastive, and framewise memory alignment objectives, yielding modest gains across different memory operations. MEMOBench provides a diagnostic evaluation suite and training supervision for memory grounded robotic policies. The project page is available at https://github.com/Collab-Gen/MEMOBench.

cs.RO↗

arXiv2025

Multimodal Spiking Neural Network for Space Robotic Manipulation

This paper presents a multimodal control framework based on spiking neural networks (SNNs) for robotic arms aboard space stations. It is designed to cope with the constraints of limited onboard resources while enabling autonomous manipulation and material transfer in space operations. By combining geometric states with tactile and semantic information, the framework strengthens environmental awareness and contributes to more robust control strategies. To guide the learning process progressively, a dual-channel, three-stage curriculum reinforcement learning (CRL) scheme is further integrated into the system. The framework was tested across a range of tasks including target approach, object grasping, and stable lifting with wall-mounted robotic arms, demonstrating reliable performance throughout. Experimental evaluations demonstrate that the proposed method consistently outperforms baseline approaches in both task success rate and energy efficiency. These findings highlight its suitability for real-world aerospace applications.

cs.RO↗

arXiv2024

Invisible Image Watermarks Are Provably Removable Using Generative AI

Invisible watermarks safeguard images' copyrights by embedding hidden messages only detectable by owners. They also prevent people from misusing images, especially those generated by AI models. We propose a family of regeneration attacks to remove these invisible watermarks. The proposed attack method first adds random noise to an image to destroy the watermark and then reconstructs the image. This approach is flexible and can be instantiated with many existing image-denoising algorithms and pre-trained generative models such as diffusion models. Through formal proofs and extensive empirical evaluations, we demonstrate that pixel-level invisible watermarks are vulnerable to this regeneration attack. Our results reveal that, across four different pixel-level watermarking schemes, the proposed method consistently achieves superior performance compared to existing attack techniques, with lower detection rates and higher image quality. However, watermarks that keep the image semantically similar can be an alternative defense against our attacks. Our finding underscores the need for a shift in research/industry emphasis from invisible watermarks to semantic-preserving watermarks. Code is available at https://github.com/XuandongZhao/WatermarkAttacker

cs.CR↗

arXiv2024

Remote Keylogging Attacks in Multi-user VR Applications

As Virtual Reality (VR) applications grow in popularity, they have bridged distances and brought users closer together. However, with this growth, there have been increasing concerns about security and privacy, especially related to the motion data used to create immersive experiences. In this study, we highlight a significant security threat in multi-user VR applications, which are applications that allow multiple users to interact with each other in the same virtual space. Specifically, we propose a remote attack that utilizes the avatar rendering information collected from an adversary's game clients to extract user-typed secrets like credit card information, passwords, or private conversations. We do this by (1) extracting motion data from network packets, and (2) mapping motion data to keystroke entries. We conducted a user study to verify the attack's effectiveness, in which our attack successfully inferred 97.62% of the keystrokes. Besides, we performed an additional experiment to underline that our attack is practical, confirming its effectiveness even when (1) there are multiple users in a room, and (2) the attacker cannot see the victims. Moreover, we replicated our proposed attack on four applications to demonstrate the generalizability of the attack. Lastly, we proposed a defense against the attack, which has been implemented by major players in the VR industry. These results underscore the severity of the vulnerability and its potential impact on millions of VR social platform users.

cs.CR↗