arXiv subjects

Yang Zhang

Publications and source records attributed to Yang Zhang.

At least 19 recordsLinked to original sources

arXiv2026

αDepth: Learning Scene-Level Soft Boundary Decomposition for Stereo Conversion

Accurately modeling soft boundaries, e.g., hair and defocus blur, is a fundamental challenge in stereo conversion due to the ambiguous blending of foreground and background. Existing depth models primarily predict single-layer depth, leading to ambiguity in depth correspondence at soft boundaries. While matting techniques can capture opacity for layered modeling, they often struggle in complex scenes with multiple targets and usually require user intervention. This paper introduces αDepth, a layered representation that decomposes soft boundaries for high-fidelity stereo conversion. Specifically, we first resolve mixed color and depth ambiguity by estimating layered color and depth values at soft boundaries. Considering complex multi-target scenes, we design a Circular Alpha Representation (CAR) that shifts the paradigm from global target extraction to local boundary decomposition. Unlike prior matting methods restricted to a single foreground/background, CAR enables efficient scene-level inference without manual guidance. Extensive evaluations demonstrate that αDepth achieves state-of-the-art performance in stereo conversion, eliminating background bleeding and structural distortions at soft boundaries.

cs.CV↗

arXiv2026

Beyond Unit-Circle Eigenvalues: Invariant Bases for Stable State Space Dynamics

Diagonal state space models process long sequences at linear cost, yet the same transition is applied at every step of a rollout, so an unconstrained recurrence holds no quantity fixed and the state decays or grows without bound. Current designs constrain one quantity, and preserving one does not guarantee preservation of the others, so a model can hold its norm to machine precision while momentum drifts by hundreds of percent. We claim that the conserved quantities of a diagonal recurrence are layered, that each layer requires a strictly stronger condition on the operator, and that a unit-modulus spectrum reaches only the outermost. Acting on the claim, we enforce the conditions required for every layer by construction. The generator of the Kronecker-Cayley transition (KC) is a graph Laplacian over object subspaces, $\mathbf{H}=\mathbf{L}\otimes I_m$, advanced by a Cayley transform, and the step stays diagonal at $O(N^3+N^2m)$ cost. The conserved set is exactly a $2m^2$-dimensional family of quadratic forms, held at every parameter value, so the quantities and the dynamics the construction reaches are fixed before training. On four conservation benchmarks, KC holds total mass at or near the reference floor while every diagonal baseline loses it by orders of magnitude. We further compare against mass correction and find that two correctors fail once the underlying prediction diverges, which shows that assembly rather than correction is necessary for a guarantee that holds across the rollout. A free modal basis trained under a conservation penalty conserves only by freezing its spectrum, whereas the assembled operator conserves with every mode but one free to rotate. Beyond these benchmarks, KC holds the linear momentum of three $N$-body systems and, at the operator level, the conserved quantity in molecular dynamics, chemical kinetics, pharmacokinetics and power-grid swing dynamics.

cs.LG↗

arXiv2026

LoopKV: Loop-Aware KV Cache Compression for Serving Looped Transformers

Looped transformers apply a shared block several times per token to improve accuracy without adding parameters. For models such as Ouro that keep a separate key-value (KV) cache per loop, cache memory limits the batch size that amortizes repeated weight reads. Uniform-bit quantization and fixed rules per loop do not allocate precision according to each layer's sensitivity in each loop. Measurements on Ouro show that quantization sensitivity varies across (loop, layer) cells, and the sum of individual cell damages approximates the overall output damage. Sharing damage also varies by cell, with later loops generally tolerating sharing better. Skipping low-attention values to save reads produces errors that compound across loops. LoopKV uses a one-time offline profile to choose 4-bit storage, 2-bit storage or reuse of the final loop's KV for each cell. The planner minimizes reads under budgets for predicted damage and storage. During decoding, each loop reads the high 2 bits of each 4-bit value and fetches the low bits only where its own attention weights require 4-bit precision. A single fused kernel in vLLM executes the plan. Serving Ouro-1.4B on one RTX PRO 6000, the memory-optimized LoopKV-M plan stores 35% less KV than uniform 4-bit quantization (KIVI-4) on the same fused kernel and CUDA graph. It sustains 1.7-2.2x KIVI-4's goodput under a latency service-level objective with a 16K-token maximum length. None of its averages on six public benchmarks falls significantly below BF16's.

cs.LG↗

arXiv2026

Rondo: Controlling Reordering in High-Throughput Packet Spraying

Modern datacenter applications demand high network bandwidth, motivating packet-level multipath transport to exploit the aggregate capacity of multiple paths. However, packet reordering under packet spraying poses challenges for hardware transports. Excessive packet reordering makes it difficult to infer packet loss, imposes substantial pressure on NIC resources, and ultimately degrades performance. Existing transports either prioritize throughput without controlling packet reordering, or mitigate reordering by restricting transmissions, which leaves available path capacity unused. In this paper, we identify two fundamental sources of packet reordering: transient inter-path queueing differences caused by control dynamics, and persistent path-delay differences due to path heterogeneity. These observations motivate a joint design of a multipath controller and a packet scheduler to address transient and persistent delay differences, respectively. We present Rondo, a packet-level multipath transport that achieves both high throughput and low packet reordering. Its multipath controller uses ACK-based per-path capacity estimation and probing to maintain stable path loads, while its packet scheduler adjusts packet transmission order to compensate for persistent delay differences. Testbed experiments and large-scale simulations show that Rondo reduces the maximum out-of-order degree by up to 97.5% while maintaining high throughput. With constrained receiver buffers, Rondo achieves up to 8.51 times higher goodput.

cs.NI↗

arXiv2026

RGB Photo Enhancement with Deep Learning on Smartphone GPUs: Mobile AI 2026 Challenge Report

On-device RGB photo enhancement is a popular task that is needed for quality improvement of photos captured with mobile devices as well as for retouching of images acquired through other sources. This creates a need for efficient deep learning models for this problem that are not only performant but can also be deployed on constrained mobile hardware. In this Mobile AI challenge, we address this problem and propose participants to design fast solutions that can perform comprehensive enhancement of RGB photos while running on popular smartphone GPUs from Qualcomm and Arm. For this, the participants were provided with a large-scale DPED dataset consisting of RGB image pairs captured with an old iPhone 3GS smartphone and a professional Canon 70D DSLR camera. The runtime of all models was evaluated on the Snapdragon 8 Elite Gen 5 and Dimensity 9500 chipsets with the Adreno and Mali GPUs, respectively. Top solutions proposed in this challenge are able to enhance Full HD resolution RGB images on both platforms under 300 milliseconds. A comprehensive description of the models developed in the challenge is provided in this paper.

cs.CV↗

arXiv2026

TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation

Generative recommendation formulates next-item prediction as autoregressive generation over semantic ID (SID) sequences derived from users' historical interactions, making modern recommender systems structurally similar to large language models (LLMs). As privacy and safety concerns grow, these systems increasingly require concept unlearning to remove sensitive or harmful concepts associated with items. However, existing LLM unlearning methods cannot be directly applied to generative recommendation. Unlike word tokens with explicit semantics, SIDs are abstract identifiers that are often shared by both forget and retain items, leading to severe conflicts between concept removal and recommendation utility preservation. To address this challenge, we propose TRACER, an end-to-end concept unlearning framework based on token reassignment. Rather than directly suppressing shared SIDs, TRACER reassigns concept-related items to alternative tokens that better facilitate forgetting while minimizing side effects on retained items. We further introduce a coherence regularizer to preserve semantic consistency among retain items during unlearning. Experiments on real-world recommendation datasets demonstrate that TRACER effectively removes target concepts while substantially better preserving recommendation utility than existing unlearning baselines.

cs.IR↗

arXiv2026

CoworkOS:Scaling Agent Coworkers for Real-world Automation

Rapid advances in cowork agents are shifting human-AI collaboration toward real-world automation, delivering software, designs, and other artifacts. However, such productivity work demands both high-quality delivery on complex productivity tasks and continued monitoring and decision-making over long horizons, a combination that remains an open challenge. We introduce CoworkOS, a general-purpose harness for long-horizon work that separates task strategy from execution management: the model decides actions, delegation, and acceptance, while the runtime maintains state and feedback. Its three-layer architecture enables direct execution or delegation to Subagents, synchronously or asynchronously, under stable responsibilities. Through a shared application layer, humans and agents inspect and revise the same evolving documents, presentations, CAD models, and Blender scenes, so human-in-the-loop participation can enter both through conversation and through direct modification of the work itself. To sustain continuity across agents and sessions, CoworkOS further introduces hierarchical workspaces and persistent semantic memory, so working files, key decisions, and long-running Job logs remain shareable, reusable, and resumable after interruption. Across 12 benchmarks and 6 categories of real-world applications, CoworkOS proves highly competitive with frontier agent applications such as Codex and DeepSeek Harness. As a sustained-work case study, CoworkOS uses GLM-5.3 as its backbone to conduct RSI-driven automated post-training on Qwen3.6-35B-A3B. Over five rounds, the model's average score across eight benchmarks rose from 33.19 to 42.95 (+9.76). Looking ahead, we believe CoworkOS opens a meaningful path toward general cowork-agent intelligence.

cs.AI↗

arXiv2026

AlgoPilot: Cross-Paradigm Reasoning in Language Models via Strategy Selection and Guidance

Real-world problems span diverse domains, including program synthesis, symbolic reasoning, planning, and optimization, and often require fundamentally different solution paradigms. A central challenge for LLM-based reasoning is twofold: identifying an appropriate problem-solving approach and formulating it correctly. In this paper, we propose AlgoPilot, a cross-paradigm reasoning framework that enables adaptive selection of the solving approaches and structured problem formulation prior to execution. The AlgoPilot framework contains three components: 1) a SteerLM, trained via supervised fine-tuning (SFT) and reinforcement learning (RL), that adaptively selects the solving strategy (paradigm and modes) based on input problems, 2) the corresponding expert GuideLM, trained via SFT, that generates structured formulation guidance tailored to the selected strategy, and 3) an ExecutionLM that generates solutions conditioned on both the selected strategy and its formulation. Unlike prompting-based methods that rely on fixed reasoning templates, our approach learns to adaptively choose and structure problem-solving strategies based on the input. We build AlgoPilot on Qwen3-8B and evaluate across 5 problem categories and 13 benchmarks with 66 unique domains, comparing against both the latest similarly scaled small models and large LLMs. AlgoPilot significantly improves average accuracy for the same model from 38.4% to 72.9%, outperforming the best large LLM prompting baseline GLM-5 by 11.7%. Ablation studies confirm the effectiveness of each component. Notably, we show that the learned steering and formulation modules can transfer across execution models, suggesting their potential to improve other LLMs. Project page: https://sites.google.com/view/algopilot.

cs.AI↗

arXiv2026

CompliantBot: Learning Whole-Body Compliance across Task Interfaces for Humanoid Control

Humanoid controllers need the ability to yield to external forces while retaining the capacity to resume their original tasks. However, tracking objectives penalize the deviations needed for yielding, while relaxing tracking goals persistently can make task recovery harder. Existing approaches often rely on task-specific interaction command interfaces or constructed compliant motion data and reference trajectories, limiting the direct reuse of their compliance-training mechanisms across different task interfaces. We introduce CompliantBot, a compliance-training framework for motion tracking and velocity-command locomotion. It preserves the original task inputs and requires no preconstructed compliant data or target trajectories. A state-dependent interaction force field couples applied loads to the robot's motion, enabling local articulation and whole-body movement to change the forces experienced. A complementary force-magnitude reward encourages lower interaction loads, while task rewards are downweighted during loading and restored after release to coordinate yielding and task resumption. The trained policies use task inputs and proprioceptive history without explicit external-force measurements. Across both task interfaces, CompliantBot yields farther under constant pulls and experiences lower spring interaction loads than matched baselines, while resuming task tracking after force removal. CompliantBot achieves the highest task completion among the compared methods under stronger pulls. Power evaluations further show that CompliantBot has the lowest mean joint mechanical power among the compared methods. Real-robot demonstrations further illustrate compliant responses through arm motion, stepping, and body reorientation.

cs.RO↗

arXiv2026

Efficient Low-Rank Approximation of Quaternion Matrices via Adaptive Cross Approximation

The adaptive cross approximation (ACA) algorithm is a powerful tool for constructing low-rank matrix approximations using only a small subset of matrix entries. While extensively studied for real and complex matrices, its extension to quaternion algebras, remains largely unexplored. This paper presents the first comprehensive framework for adaptive cross approximation operating natively in the quaternion algebra H. Unlike approaches that rely on real embeddings or complexifications, our method preserves the algebraic structure of quaternions throughout the computation, avoiding information loss and maintaining the natural correlations between quaternion components. We develop a fully quaternionic ACA algorithm with rigorous theoretical foundations, including convergence analysis, error bounds, and complexity estimates. The algorithm achieves O(r(m+n)) complexity per iteration and demonstrates quasioptimal approximation properties comparable to the quaternion singular value decomposition (QSVD), but with significantly reduced computational cost. We provide a detailed MATLAB with numerical experiments on color image compression. Our results show that native quaternion ACA outperforms significantly the basic SVD algorithm in terms of computational efficiency, with very close approximation accuracy.

math.NA↗

arXiv2026

Split Quaternion Toolbox: A MATLAB Implementation

This paper presents a MATLAB toolbox for working with split quaternions and split quaternion matrices. Split quaternions, also known as coquaternions, form a four-dimensional associative non-commutative algebra over the real numbers with a signature (+,+,-,-). The toolbox provides two main classes: SplitQuaternion for scalar-valued split quaternions and SplitQuaternionMatrix for matrix-valued split quaternions. The implementation includes arithmetic operations, algebraic functions, matrix operations, linear algebra utilities, and various conversion methods. The toolbox is designed to be intuitive for users familiar with MATLAB's standard matrix operations while providing the full power of split quaternion algebra. This paper details the implementation, provides numerous examples, and demonstrates the toolbox's capabilities through practical demonstrations. The MATLAB toolbox is accessible at https://github.com/SalmanAhmadi-Asl/Split_Quaternion.git.

math.NA↗

arXiv2026

Select-TTT: Query-Conditioned Selective Fast Weight Updates for In-Place Test-Time Training

Test-time training (TTT) enables a deployed language model to internalize incoming context on-the-fly into a small set of fast-weight parameters, rather than merely treating it as transient context for attention. In-Place TTT recently made this idea practical for large language models by repurposing the down-projection of standard MLP blocks as fast weights. However, we find that its benefit largely disappears on realistic web long-context question answering. We attribute this to In-Place TTT's uniform write schedule: every chunk is written with equal strength, so irrelevant content dilutes query-relevant information in fast-weight memory. Building on this insight, we propose Select-TTT, a model-agnostic, query-conditioned rule for In-Place TTT that scales each chunk's write strength by its relevance to the user query. This relevance signal requires no supervision and no new parameters: it is read directly from the model's frozen output head, as the probability mass that a chunk's hidden states assign to the query tokens. A temperature-scaled softmax turns these scores into learning-rate multipliers, leaving the chunk-wise, context-parallel computation of In-Place TTT intact. Select-TTT consistently improves long-context question answering on two benchmarks and two backbones. These findings highlight the value of query-conditioned fast weight writes for practical test-time training on long-context question answering, which is also potentially promising for long-horizon agent tasks involving multiple user queries over evolving contexts.

cs.LG↗

arXiv2026

Probing the singlet-triplet scotogenic model with lepton flavor violation, gravitational waves and dark matter

We perform a comprehensive phenomenological analysis of neutrino masses, lepton-flavor violation, gravitational-wave signature induced by the first-order electroweak phase transition, and scalar dark matter within the framework of the singlet--triplet scotogenic model. We derive general analytical expressions for the one-loop Wilson coefficients of the penguin and box contributions to lepton-flavor-violating processes, considering generic scalar and fermion mediators. These general results are then applied to the singlet--triplet scotogenic model to calculate the branching ratios of $μ\to eγ$ and $μ\to e\overline{e}e$, as well as the coherent $μ$--$e$ conversion rate. We systematically investigate the lepton-flavor-violation phenomenology and the constraints from current and future experiments. It is found that the coupling $λ_5$ plays a key role in connecting neutrino masses, lepton flavor violation, and dark matter direct detection. We also study the finite-temperature effective potential and the gravitational-wave signatures of the resulting first-order phase transition. By analyzing the thermal history of the model, we identify the viable parameter space allowing for a first-order electroweak phase transition in the early Universe and find that the portal parameters $λ_1^Ω$ and $μ_1$ play a crucial role in determining the transition strength. The corresponding stochastic gravitational-wave spectra can be probed by future gravitational-wave observatories. As a representative example, we identify a benchmark point that simultaneously satisfies the current constraints from lepton-flavor-violation and dark-matter experiments and generates a stochastic gravitational-wave spectrum that may be probed by the Laser Interferometer Space Antenna, the Big Bang Observer, and the Ultimate Deci-hertz Interferometer Gravitational-wave Observatory.

hep-ph↗

arXiv2026

Rollback: Experience Backtracking in World Models for Policy Improvement

Action-conditioned world models offer a scalable alternative to costly real-world interaction for improving robot policies. However, existing world-model-based methods largely treat world models as forward-only surrogates of the physical environment, leaving untapped a capability no physical environment affords: rewinding to past decision points to explore counterfactual futures. We introduce Rollback, a policy improvement paradigm that turns world models from passive simulators into counterfactual engines: it rolls back to critical decision points, branches into alternative actions, and contrasts their outcomes to attribute success to individual decisions. This concentrates imagination where it matters most and distills counterfactual outcomes into fine-grained, action-level supervision without a separately learned value model. We build a scalable training system that unifies distributed world-model search with asynchronous real-robot execution, forming a self-reinforcing cycle: real-world experience continually sharpens the world model, and a sharper world model in turn drives further policy improvement. We term the world-model side of this cycle test-time physics grounding, a capability that hand-built physics simulators lack: while the world model serves as the training environment, real transitions from the policy it trains correct its dynamics through a lightweight LoRA pipeline. Experiments on three challenging real-world tasks show that Rollback improves success rates over the strongest baseline by an absolute 10.67%, 11.11%, and 11.85% on block extraction, ball tossing, and tabletop curling, respectively, while achieving more stable improvement throughout training. Although initialized from teleoperated demonstrations far below human level, Rollback surpasses the average success rate of human players on both ball tossing and tabletop curling within 8 hours of training.

cs.RO↗

arXiv2026

WorldOracle: Benchmarking Action-Conditioned World Models against Real-World Executions

We introduce WorldOracle, a benchmark specifically designed to evaluate action-conditioned world models (ACWMs) against paired real-world executions. Given the same initial observation and low-level action sequence, WorldOracle asks whether a predicted rollout reproduces what happens in the real world, including physical consequences and task-state transitions. We evaluate models along four complementary dimensions: physical state correctness, interaction spatial alignment, key interaction area fidelity, and multi-view action coherence. To support this evaluation, we curate real-world training and evaluation suites that cover different interaction dynamics and embodiments. The evaluation suite contains long, multi-segment episodes with both successful and failed executions, enabling evaluation across varied action outcomes and behavior distributions. Experiments on seven representative ACWMs reveal distinct model strengths and failure modes. WorldOracle provides a systematic foundation for measuring and advancing action-conditioned world modeling for manipulation. We release the datasets, evaluation code, and leaderboard: https://worldoracle-benchmark.github.io/.

cs.RO↗

arXiv2026

Exact Magnetic Confinement of a Doped Hole by Kinetic Frustration

We establish an exact mechanism of magnetic confinement driven purely by kinetic frustration. In a finite open sawtooth $U=\infty$ Hubbard chain of $L$ unit cells doped with one hole, the ground state at magnetization $M$ contains $M$ polarized triplet dimers surrounding a connected singlet region of length $\ell_{\rm s}=L-M$. A magnetic field selects $M$ and hence the confinement length. The hole forms a standing wave in the singlet region with exponential tails in the triplet background. Confinement requires neither disorder nor an exchange-generated string tension: destructive interference between direct and apex-mediated hopping makes the triplet bonds reflecting. At $t_2/t_1=[2+4\cos(π/(\ell_{\rm s}+2))]^{-1/2}$, they become perfectly reflecting, producing a position-degenerate compact localized state. At every fixed field $0<h<h_{\rm sat}$, the singlet region remains finite as $L\to\infty$ and shrinks stepwise with increasing field, whereas at $h=0$ it spans the system.

cond-mat.str-el↗

arXiv2026

Distributions or Quantiles? A Convex Variational Theory of Worst-Case Risk

Worst-case risk evaluation is central to finance and operations research, yet existing analytical results remain fragmented and largely confined to ambiguity sets with specific structures. Using spectral risk measures as the underlying class, we develop a convex variational theory that unifies these analyses and extends them to richer ambiguity models. The theory rests on a representation principle: use the quantile or distribution formulation that preserves convexity of the full ambiguity set. In both representations, the resulting optimality conditions provide a systematic route to identify the worst-case law and determine its risk value. This approach recovers classical central-moment bounds and yields closed-form or semi-closed-form solutions for practically motivated models involving Wasserstein ambiguity with tail stress, two-sided expected shortfall (ES) bands, asymmetric partial moments, geometric-moment bounds, and exponential- or Gaussian-reference Kullback--Leibler (KL) neighborhoods. Beyond these analytical results, the same theory yields projection-based algorithms with fixed-grid convergence under the stated assumptions. In a high-dimensional portfolio application, we establish an exact projection identity for mean-anchored joint KL ambiguity under a Gaussian reference. The resulting standardized worst-case ES coefficient applies to every portfolio, reducing robust portfolio selection to an exact second-order cone program after a single calibration.

q-fin.RM↗

arXiv2026

Prediction of the Geoneutrino Signal at the Jinping Neutrino Experiment

We present refined predictions of the geoneutrino flux for the 500-tonne Jinping Neutrino Experiment (JNE). We construct a local crustal model with a resolution of $0.2^\circ\times0.2^\circ$ based on the SWChinaCVM-2.0 seismic velocity model and integrate it with CRUST1.0 to establish our reference model. For comparison, we also calculate the geoneutrino flux using ECM1 and CRUST1.0 as standalone global crustal models. Assuming an energy scale of 500~p.e./MeV and an exposure of 500~t$\times$1500~days, we predict the inverse beta decay event spectrum, including both the geoneutrino signal and the reactor antineutrino background. The distinct spectral shapes of the $^{238}$U, $^{232}$Th, and reactor antineutrino components allow their respective contributions to be obtained through spectral fitting. These predictions provide inputs for the JNE data analysis, particularly for extracting the mantle geoneutrino signal and testing competing bulk silicate Earth models.

hep-ex↗