arXiv subjects

Yang Chen

Publications and source records attributed to Yang Chen.

At least 55 records · Page 3Linked to original sources

arXiv2026

OTTER - Two Transistor - One RRAM Architecture for Reliable In-Memory-Computing in 28 nm CMOS Technology

This work presents OTTER, a 28 nm CMOS platform co-integrated with TaOx-based valence-change mechanism (VCM) RRAM, demonstrating a two-transistor-one-memristive-device (2T1R) architecture for reliable in-memory computing. The 2T1R cell combines a low-drive-current (LD) transistor and a high-drive-current (HD) transistor in parallel, providing dedicated bias paths for SET programming and RESET operation, respectively. Through systematic experimental and simulated comparison of various transistor-pairing configurations using the physical compact model JART VCM Rth, design guidelines for transistor sizing are derived, establishing the minimum RESET transistor W/L required for complete RESET as a function of the SET current compliance. The 2T1R cell is further characterized under pulse-based programming, demonstrating multilevel analog conductance tuning with narrow, well separated conductance states across six programmable levels. An analog content-addressable memory (aCAM) design based on the same 2T1R cell is additionally analyzed at the circuit level, evaluating trade-offs between top- and bottom-connected RRAM comparator configurations. A hardware implementation of compute-in-memory (CIM) multiply-and-accumulate (MAC) operations is further demonstrated on a 15 x 15 2T1R crossbar array.

cs.ET↗

arXiv2026

LookStep: Efficient Vision-Language Navigation with Linguistic Foresight and Event Driven Memory

Vision-Language Navigation (VLN) requires an embodied agent to follow natural-language instructions in unseen environments. Recent progress has been largely driven by Multimodal Large Language Models (MLLMs). Existing methods follow a next-step action prediction paradigm, supervising only the expert action, which requires a high quantity of data for training. They also rely on cognitive maps, accumulated historical frames, or external 3D tools to maintain states, leading to high computational and memory overhead. To realize resource efficiency VLN, we propose LookStep, a unified end-to-end framework that combines Language Centric Future State Modeling and Event Driven Rolling Memory that uses language labels to generate coarse-grained navigation progress and future states for each candidate action, while autonomously deciding whether to write each observation into a bounded rolling memory with a semantic role. We validate LookStep empirically. On VLN-CE tasks, LookStep outperforms existing methods under the same training settings, achieving a 49.7\% success rate on R2R-CE Val-Unseen with better memory efficiency and less data usage. Code and model is available at https://github.com/kunyang-YU/LookStep.

cs.CV↗

arXiv2026

GramLoop: Training-Free Gram-Gated Replay for Robust Dense Prediction

We aim to improve frozen DINOv3 dense-prediction models under distribution shift by adding inference computation inside the visual backbone, without changing model weights, task adapters, or prediction heads. The challenge is that repeated transformer-block computation must refine dense features without disrupting the pairwise patch relations that DINOv3 uses to preserve spatial structure. We introduce GramLoop, a training-free framework that replays a short transformer window and controls each replay through final-layer cosine-Gram consistency. Each proposal is propagated through the frozen suffix, measured against the standard DINOv3 trajectory, and accepted through a patchwise gate at the replay-window endpoint. Across object detection and semantic segmentation under corruptions, perturbations, and natural shifts, GramLoop improves all five shifted benchmarks over the paired DINOv3 baseline. On COCO-O, it improves mAP by +0.252 and Effective Robustness by +0.250, while preserving clean ADE20K performance. Code will be released at https://github.com/cheyan9/GramLoop.

cs.CV↗

arXiv2026

LHAASO-WCDA observed a $\sim$ 5 days TeV-delayed flaring event in blazar 1ES 1959+650

We report a day-scale hard lag between GeV and TeV $γ$-ray emission from the HBL 1ES~1959+650 in early 2024. Since the LHAASO-WCDA real-time monitoring system began operation in late 2023, multiple TeV flares from this source have been triggered, including the 1st trigger flare on 2024 February 9. A Bayesian-block analysis of the WCDA light curve identifies three TeV flares in 2024. For the second triggered flare, a discrete cross-correlation analysis reveals a $>3\,σ$ correlation (relative to uncorrelated red-noise simulations) at a time delay of $Δt = 5.0_{-2.1}^{+2.1}$ days, with the TeV emission lagging the GeV. Time-resolved spectroscopy shows that this flare has the softest TeV spectrum among these flares (intrinsic spectral index $Γ=3.16\pm0.18$), while the 1st trigger flare is harder ($Γ=2.48\pm0.21$). The observed five-day hard lag is difficult to reconcile with a purely cooling-driven temporal ordering and is consistent with scenarios in which particle energization and/or transport may contribute to the evolution. However, the current data do not uniquely identify the underlying mechanism.

astro-ph.HE↗

arXiv2026

Harness-of-Harness: Multi-Day Autonomous Software Development with Continual Improvement

This paper studies autonomous software development, in which LLM-based coding agents transform high-level requirements into complete, functional, and usable software systems without human intervention. We introduce Harness-of-Harness (HoH), a framework that enables coding agents to continually improve software during autonomous development. HoH operates on existing coding-agent harnesses, and organizes their executions into iterative planning-coding-testing loops. To sustain improvement across loops, HoH balances repair with capability growth, scopes development into small and verifiable increments, separates implementation-time testing from independent evaluation, and constrains verifiable outputs rather than prescribing agent workflows. It progressively exposes deliverables, role-specific tools, and skills, encourages reuse rather than recreation, and maintains versioned project histories. On GameCraft-Bench, FrontierSWE, and ProgramBench, three harness-model pairs (Codex with GPT-5.5, OpenCode with DeepSeek-V4-Pro, and Pi with MiniMax-M3), HoH consistently outperforms the corresponding standalone harnesses, achieving an average relative gain of 52.25 percent and a maximum gain of 82.86 percent after three iterations. In a multi-day deployment with more than 70 iterations, HoH autonomously develops a first-person-shooter game, featuring a coherent storyline, fully implemented core mechanics, human-playable experience, polished visuals and integrated audio. Github: https://github.com/Flesymeb/HarnessOfHarness Project Page: https://flesymeb.github.io/HarnessOfHarness/

cs.AI↗

arXiv2026

R3G: A Reasoning-Retrieval-Reranking Framework for Vision-Centric Answer Generation

Vision-centric retrieval for VQA requires retrieving images to supply missing visual cues and integrating them into the reasoning process. However, selecting the right images and integrating them effectively into the model's reasoning remains challenging. To address this challenge, we propose R3G, a modular Reasoning-Retrieval-Reranking framework. It first produces a brief reasoning plan that specifies the required visual cues, then adopts a two-stage strategy, with coarse retrieval followed by fine-grained reranking, to select evidence images. On MRAG-Bench, R3G improves accuracy across six MLLM backbones and nine sub-scenarios, achieving state-of-the-art overall performance. Ablations show that sufficiency-aware reranking and reasoning steps are complementary, helping the model both choose the right images and use them well. We release code and data at https://github.com/czh24/R3G.

cs.CV↗

arXiv2026

Training-Free Hidden-State Refinement for Flow-Matching Image Generators

We aim to improve frozen flow-matching image generators by adding inference computation inside the denoiser, without changing model weights or the outer sampler. Existing generators usually spend extra test-time computation by increasing the number of sampling steps, which repeatedly evaluates the entire denoiser and couples quality gains to sampler cost. A key challenge is how to use extra computation inside a frozen transformer denoiser: the method must decide which tokens, layers, and sampling times receive repeated updates while preserving the original generation pipeline. We introduce a training-free looping framework that repeatedly applies selected transformer layers inside each denoising call. Dense and Sparse Token Loop vary the token scope; Sampling-Progress Gating and the loop layer range specify when and where looping is active; loop count and strength control the repeated updates; and Loop Guidance combines ordinary and looped vector-field predictions. Across two Scale-RAE model scales, loop variants improve primary and auxiliary quality metrics with competitive quality--efficiency trade-offs. Loop Guidance further improves both primary metrics across all three tested models; on Scale-RAE DiT2.4B, it raises GenEval from 0.4471 to 0.5691 and DPG-Bench from 0.7656 to 0.8053. Code will be released.

cs.CV↗

arXiv2026

PAPO: Stabilizing Rubric Integration Training via Decoupled Advantage Normalization

We propose Process-Aware Policy Optimization (PAPO), a method that integrates process-level evaluation into Group Relative Policy Optimization (GRPO) through decoupled advantage normalization, to address two limitations of existing reward designs. Outcome reward models (ORM) evaluate only final-answer correctness, treating all correct responses identically regardless of reasoning quality, and gradually lose the advantage signal as groups become uniformly correct. Process reward models (PRM) offer richer supervision, but directly using PRM scores causes reward hacking, where models exploit verbosity to inflate scores while accuracy collapses. PAPO resolves both by composing the advantage from an outcome component Aout, derived from ORM and normalized over all responses, and a process component Aproc, derived from a rubric-based PRM and normalized exclusively among correct responses. This decoupled design ensures that Aout anchors training on correctness while Aproc differentiates reasoning quality without distorting the outcome signal. Experiments across multiple model scales and six benchmarks demonstrate that PAPO consistently outperforms ORM, reaching 51.3% vs.\ 46.3% on OlympiadBench while continuing to improve as ORM plateaus and declines.

cs.AI↗

arXiv2026

CoCoBench: A Cooperative Coordination Benchmark for Embodied Multi-Agent Task Planning

Agent systems powered by multimodal large language models (MLLMs) have advanced rapidly in recent years, yet existing embodied-agent benchmarks still lack fine-grained diagnostics for multi-agent coordination. Most benchmarks either focus on single-agent task completion or summarize multi-agent behavior with overall task success rates, which can obscure coordination failures such as duplicated work, violations of ordering constraints, resource contention, and desynchronized handoffs. In this paper, we introduce CoCoBench, a construct-level benchmark for evaluating multi-agent embodied coordination in executable household tasks. CoCoBench contains 897 oracle-validated instances organized around four recurring coordination constructs: task allocation, sequential ordering, mutual exclusion, and handoff coordination. In addition to task success rate, CoCoBench provides construct-level scores that measure whether agents coordinate effectively. We evaluate 11 leading MLLMs across different coordination modes, observation inputs, and numbers of agents. The results show that coordination ability is highly construct-specific: strong overall performance does not imply balanced competence across different coordination types. These findings point to new directions for designing targeted model architectures and improving multi-agent coordination ability.

cs.RO↗

arXiv2026

STEGNav: Spatio-Temporal Event Graph Reasoning for Multimodal Lifelong Object Navigation

Multimodal lifelong navigation requires an agent to autonomously explore unseen environments while sequentially completing navigation tasks specified by object categories, language descriptions, or reference images. Existing methods primarily accomplish these tasks by constructing state-centric semantic scene graphs. By treating scene graphs as persistent repositories of semantic observations, these methods struggle to distinguish similar instances, jointly represent semantic targets and exploration frontiers, and effectively exploit navigation memory and trajectory experience. To address these limitations, we propose Spatio-Temporal Event Graph Navigation (STEGNav), a training-free framework that extends conventional scene graphs into spatio-temporal event graphs along complementary spatial and temporal axes. The spatial axis performs query-conditioned instance grounding and jointly represents semantic targets and occupancy-aware exploration frontiers characterized by reachability, path cost, and exploration utility. The temporal axis employs trajectory-aware dual-window memory to retain recent decision--trajectory events and verified cross-subtask navigation outcomes. A VLM-based navigation agent reasons over the resulting spatio-temporal event graph and selects either a target instance or an exploration frontier as its next navigation goal. STEGNav achieves 66.3% SR and 39.7 SPL on GOAT-Bench, as well as SR scores of 64.0% and 69.4% on HM3Dv1 and HM3Dv2, respectively. Ablation studies and error analyses validate the complementary effects of the two axes, demonstrating that event-driven spatio-temporal representations improve navigation reliability and cross-subtask experience reuse.

cs.RO↗

arXiv2026

Analog Time-Frequency Analysis and Frequency Measurement of Ultra-Wideband Microwave Signals via Dual-Comb Channelization

Frequency-to-time mapping (FTTM)-based photonics-assisted spectrum sensing enables wideband spectrum monitoring for applications such as cognitive radio and electronic warfare. However, FTTM-based photonics-assisted spectrum sensing approaches involve trade-offs among analysis bandwidth, temporal resolution, and frequency resolution. Although channelization has been introduced to alleviate these trade-offs, the limited number of available channels in previously reported systems constrains further improvements in overall performance. Here, we propose a photonics-assisted dual-comb channelization architecture for broadband frequency measurement and time-frequency analysis. The system employs a stepped-frequency pump based on stimulated Brillouin scattering (SBS) to generate an optical filter comb and adopts a single-laser design for the pump, probe, and reference paths to enhance frequency stability while reducing system complexity. Experimental results show a 48-GHz analysis bandwidth in the 16-channel configuration and 52 GHz in the 20-channel configuration, together with microsecond-scale temporal resolution and a frequency resolution better than 80 MHz. The proposed architecture therefore offers a practical and reconfigurable solution for real-time wideband spectrum sensing.

physics.optics↗

arXiv2026

HORIZON: A Read-Efficient Firmware for DNA Storage with Horizontal Layout

DNA storage is a promising medium for long-term archiving, but its read performance is limited by coarse-grained random access. Existing random-access DNA storage designs suffer from high read amplification because their sequential layouts co-locate frequently and infrequently accessed data under the same primer pair, where any read must retrieve all associated strands even when only a small fraction is needed. We present HORIZON, a read-efficient allocation policy for DNA block devices that reduces read amplification through activity-aware horizontal placement. HORIZON first introduces a horizontal layout distributing writes round-robin across primer pairs, rather than filling each sequentially. It classifies newly written blocks in the write buffer as active or inactive, tracks recent primer-pair accesses using a sliding-window temperature model, and allocates blocks based on block activity and primer-pair occupancy. Simulations show HORIZON consistently reduces read amplification compared with state-of-the-art schemes across MSR and FIU traces and synthetic filesystem workloads.

cs.ET↗

arXiv2026

Fluid Antenna Channel Reconstruction with Non-Negative Least Square Detector

Reconstructing the full-aperture channel of a fluid antenna system (FAS) from a few activated ports requires inferring unobserved responses from limited spatial samples. This paper proposes a covariance-domain FAS channel-reconstruction method based on nonnegative least squares (NNLS). A nonuniform port arrangement constructs a difference coarray with an enlarged aperture. Vectorizing the sample covariance maps the physical array to a virtual steering dictionary, and the resulting complex covariance-fitting model is recast as a strictly equivalent real-valued NNLS problem. A projected-gradient iteration initialized at zero estimates the nonnegative angular-occurrence coefficients, and the L_s strongest well-separated peaks yield the angles of arrival (AoAs). Simulations show that the proposed FAS-NNLS estimator exploits the enlarged virtual aperture and achieves lower AoA mean squared errors than uniform linear array (ULA) multiple signal classification (ULA-MUSIC) and ULA estimation of signal parameters via rotational invariance techniques (ULA-ESPRIT). Its non-line-of-sight (NLoS) path accuracy matches FAS covariance matching pursuit, while its line-of-sight (LoS) estimation is significantly improved.

eess.SP↗

arXiv2026

Module-Valued 2-Local Derivations on Reductive Lie Algebras

Let $\F$ be an algebraically closed field of characteristic zero, $\g=\s\oplus\z$ a finite-dimensional reductive Lie algebra over $\F$, and $V$ an arbitrary finite-dimensional $\g$-module. We classify all 2-local derivations of $\g$ on $V$, and show that every 2-local derivation is a derivation if and only if $\dim\z\leq1$ or $V^\g=0$. If $\dim\z\geq2$ and $V^\g\ne0$, the nonlinear homogeneous maps give all exceptional 2-local derivations.

math.RA↗

arXiv2026

Self-Harness: Harnesses That Improve Themselves

The performance of LLM-based agents is jointly shaped by their base models and the harnesses that mediate their interaction with the environment. Because different models exhibit distinct behaviors, effective harness design is inherently model-specific. Yet agent harnesses are still largely engineered by human experts, a paradigm that scales poorly as modern LLMs become increasingly diverse and rapidly evolving. In this paper, we introduce Self-Harness, a new paradigm in which an LLM-based agent improves its own operating harness, without relying on human engineers or stronger external agents. We operationalize Self-Harness as an iterative loop with three stages: Weakness Mining, which identifies model-specific failure patterns from execution traces; Harness Proposal, which generates diverse yet minimal harness modifications tied to these failures; and Proposal Validation, which accepts candidate edits only after regression testing. We instantiate Self-Harness across Terminal-Bench-2.0, SWE-bench Verified, and AppWorld using a minimal initial harness and three base models from diverse families: MiniMax M2.5, Qwen3.5-35B-A3B, and GLM-5. Across all nine model--benchmark combinations, every final harness improves both held-in and held-out pass rates, with overall relative gains of up to 132%. Qualitative analyses further show that the retained mechanisms address benchmark-specific bottlenecks in artifact handling and runtime control, software-patch verification, and application-state retrieval. These results suggest a path toward LLM-based agents that are not merely shaped by their harnesses, but can also participate in reshaping them.

cs.CL↗

arXiv2026

Learning What Not to Learn: Adversarial Disentangled Prompt Tuning for Robust Vision-Language Models

While adversarial prompt tuning can enhance robustness of vision-language models efficiently, we find that existing methods aggravate robust generalization overfitting on seen classes, leading to a rapid degradation in performance against adversarial examples of unseen classes as training progresses. We empirically identify that this degradation stems from the tendency of the model to learn pseudo-robust features (i.e., non-generalizable shortcuts). To mitigate this, we propose ADAPT (Adversarial Disentangled Prompt Tuning), a robust prompt tuning framework following the philosophy of ``Learning What Not to Learn''. Specifically, ADAPT uses a dual-prompt mechanism with a target prompt and a pool of decoy prompts. During training, the decoy prompts are guided to entrap diverse pseudo-robust features, while the target prompt is constrained to be orthogonal to the decoys in the embedding space to learn robust features. By disentangling the robust features from the pseudo-robust features, ADAPT effectively prevents robust generalization overfitting. We further provide an analysis showing that the orthogonal loss bounds the effect of shifts in pseudo-robust features on unseen classes, yielding a testing error guarantee. Empirically, extensive experiments demonstrate that ADAPT substantially improves the robustness of the target prompt on unseen classes. The code is available at https://github.com/cheny02/ADAPT-ACMMM2026.

cs.CV↗

arXiv2026

Plug-and-Play Traffic Element Awareness for End-to-End Autonomous Driving

Traffic elements such as traffic lights and road signs play a fundamental role in human driving decisions and should naturally influence end-to-end driving performance. However, existing end-to-end driving research predominantly focuses on dynamic road participants (e.g., vehicles and pedestrians), while the role of traffic elements remains largely unexplored. The community still lacks a systematic study quantifying their impact, largely because public datasets rarely provide structured traffic-element annotations and modern driving systems vary widely in architecture and training paradigm. In this work, we present the first systematic investigation of traffic element awareness for end-to-end autonomous driving. We construct a unified research infrastructure by augmenting multiple public driving datasets with comprehensive traffic-element annotations. To support diverse model families, we adopt a minimal and universal integration design that incorporates traffic-element signals into existing pipelines in a plug-and-play manner with negligible architectural modification. We evaluate this design across modern paradigms, including perception-prediction-planning pipelines, vision-language-action models (VLA), regression-based planners, diffusion-based policies, and trajectory-scoring frameworks, on nuScenes, NAVSIM-v1, NAVSIM-v2, and Bench2Drive. Across all paradigms and datasets, this simple integration consistently improves driving performance, demonstrating that traffic element awareness provides a robust and generalizable signal for end-to-end driving systems. Notably, on the challenging NAVSIM-v2 benchmark, our approach significantly improves state-of-the-art architectures and data pipelines, establishing a new state of the art.

cs.CV↗

arXiv2026

Human-Centric Intelligence in the Era of Foundation Models: A Survey

Human-centric intelligence is evolving in the foundation-model era, with growing emphasis on scale, transferability, and general-purpose modeling. Yet it has not fully integrated with foundation models to achieve the comparable progress seen in them. More importantly, recent advances across this broad landscape remain fragmented across tasks, modalities, and research communities, leaving their intrinsic conceptual and methodological connections unclear. To bridge these divides and rethink human-centric intelligence in the foundation-model era, we introduce a full-spectrum human context taxonomy that integrates six interconnected levels by viewing humans as observable subjects through visual appearance and spatial geometry, as dynamic actors through kinematic dynamics and interaction modeling, and as situated agents through world simulation and embodied agency. We next present the methodological foundations of the field, covering human-centric data families, computational architecture paradigms, and representative training and inference optimization strategies. We then systematically review representative methods across these levels and organize the associated datasets, benchmarks, and evaluation metrics. We further discuss open challenges and promising research directions toward human-centric intelligence that is scalable, trustworthy, physically grounded, and deployable, aiming to provide a coherent framework and practical reference for advancing the field. Finally, we provide a systematically organized and continuously updated collection of human-centric AI literature and resources on our project page.

cs.CV↗