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Han Xiao

Publications and source records attributed to Han Xiao.

At least 19 recordsLinked to original sources
arXiv2026

Arboricity games: the core and the nucleolus

The arboricity of a graph is the minimum number of forests required to cover all its edges. In this paper, we examine arboricity from a game-theoretic perspective and investigate cost-sharing in the minimum forest cover problem. We introduce the arboricity game as a cooperative cost game defined on a graph. The players are edges, and the cost of each coalition is the arboricity of the subgraph induced by the coalition. We study properties of the core and propose an efficient algorithm for computing the nucleolus when the core is not empty. In order to compute the nucleolus in the core, we introduce the prime partition which is built on the lattice of maximum-density edge sets. The prime partition decomposes the edge set of a graph into a non-prime set and a partially ordered collection of prime sets defined from minimal densest minors and their invariant precedence relation. Moreover, edges from the same set of the partition always have the same value in a core allocation. Consequently, when the core is not empty, the prime partition significantly reduces the number of variables and constraints required in the linear programs of Maschler's scheme and allows us to compute the nucleolus in polynomial time. Besides, the prime partition provides a graph decomposition analogous to the celebrated core decomposition and the density-friendly decomposition, which may be of independent interest.

cs.GT↗
arXiv2026

On kernel Mengerian orientations of line multigraphs

A kernel of a digraph is an independent set $K$ such that every vertex outside $K$ has an arc to a vertex in $K$. We present a polyhedral description of kernels in clique-acyclic orientations of line multigraphs. Given a digraph $D$, let $FK(D)$ denote its fractional kernel polytope, and let $σ(D)$ denote the linear system defining $FK(D)$. A digraph $D$ is called kernel perfect if every induced subdigraph has a kernel, called kernel ideal if the fractional kernel polytope of every induced subdigraph is integral, and called kernel Mengerian if the defining system of every induced subdigraph is totally dual integral. We show that a clique-acyclic orientation of a line multigraph is kernel ideal if and only if it is kernel Mengerian. These equivalent properties hold precisely when the orientation is kernel perfect and has no even-cycle obstruction.

math.CO↗
arXiv2026

Learning from Fleet Experience: Collective Experience Learning Across 96 Robots

Robot deployment generates experience that can support policy improvement, but continued reliance on expert corrective actions makes learning difficult to scale across growing fleets. We propose learning from fleet experience: using deployed robots' own executions as a source of supervision for further policy improvement. We call this paradigm Collective Experience Learning (CEL), in which robots pool execution experience to update a shared policy that returns to the fleet. Following expert-supervised initialization, we realize CEL through failure-guided experience retrieval fine-tuning (FERT). FERT uses failure contexts to retrieve relevant segments from successful trajectories and prioritizes their recorded actions for supervised updates, without collecting additional expert demonstrations or corrective actions. Operators provide binary episode-outcome labels and environment resets. A fault-tolerant asynchronous training system (FATS) coordinates robot execution, cloud inference and policy optimization despite local interruptions. Across five real-world manipulation tasks, the resulting policies achieve success rates of 93.3-99.2%, evaluated over 120 trials per task on robots excluded from training data collection. With collection time per robot held fixed, larger fleets provide more experience and generally improve performance across the evaluated sizes. On multi-stage phone packaging, learning with 96 robots raises success from 79.2% to 98.3%. These results support CEL as a scalable approach to improving robot policy reliability through experience generated during deployment.

cs.RO↗
arXiv2026

A Complete Characterization of Convexity in Flow Games

Flow games coincide precisely with non-negative totally balanced games. We give a complete characterization of convexity for flow games with one arc per player. We prove that such a game is convex if and only if all simple source-to-sink paths can simultaneously carry their full path capacities. Equivalently, after deleting null arcs, the topology is a funnel network, and every arc can meet the combined demand of the paths through it. We provide polyhedral characterizations of convexity. In primal form, the game is convex if and only if the path-flow polytope is a box, allowing each path flow to vary independently up to its capacity. In dual form, convexity is equivalent to the existence of a minimum source-to-sink cut consisting entirely of private arcs. An arc is private if it belongs to exactly one simple source-to-sink path. We also give a polynomial-time algorithm that tests whether a given flow game is convex.

cs.GT↗
arXiv2026

omni-macos: On-Device Omni-Modal Search on Apple Silicon

We present omni-macos, a search engine that embeds text, code, documents, images, audio and video into one representation space and runs its encoder, index and store on the Mac that already holds the files, so the files, the queries and the vectors stay on that machine. It keeps a background indexer and an interactive search box responsive on one shared device. Each distinct chunk is embedded and stored once, and every file becomes searchable from a small first share of its content. Indexer work is split into smaller units while the user types, and one memory setting reaches every allocator found to draw on unified memory. Once the index is large, queries scan a one-bit replica of it, rescore a shortlist exactly and rank every modality in one list on a common score scale. We measure omni-macos on five Macs spanning an eightfold range of accelerator width and a thirty-twofold range of memory, and its ranking on a labeled multilingual and multimodal testbed. omni-macos is open source under Apache 2.0 at https://github.com/hanxiao/omni-macos.

cs.IR↗
arXiv2026

Leaky-Wave Antenna for Non-Invasive Continuous Glucose Monitoring

Non-invasive continuous glucose monitoring requires sensing techniques capable of delivering stable, accurate, and real-time quantification of glucose-induced dielectric variations. This paper presents a microwave-based methodology that employs a leaky-wave antenna (LWA) to launch localized traveling electromagnetic waves and estimate glucose concentration through phase measurements at a fixed operating frequency. The LWA architecture enhances electromagnetic coupling with the material under test through controlled energy leakage, enabling strong linearity and high phase sensitivity within the physiological permittivity range (relative permittivity 68-70). In vitro measurements using fetal bovine serum-glucose samples demonstrate a total phase shift of 8.51 degree across glucose concentrations from 50 to 255 mg per liter. Preliminary in vivo experiments conducted on two human subjects show strong correlations between measured S11 phase and reference glucose levels (r=0.939 and r=0.906), confirming the potential of the LWA-based sensing mechanism to track dynamic glycemic variations in a proof-of-concept setting. These results highlight the potential of the proposed approach for practical, continuous, and non-invasive glucose monitoring.

physics.app-ph↗
arXiv2026

What Transfers from a VLM Teacher? Comparing Supervision Signals for Visual Document Retrieval

Visual document retrievers are trained contrastively: each query is matched to one page labelled relevant - the positive - and pushed away from negatives, pages presumed irrelevant. Recent methods distil a vision-language model (VLM) teacher into the retriever by enriching that positive, transferring the teacher's attention over it or a description of it. We ask whether the teacher is better spent on the other side, judging the candidates the retriever mines as negatives, which the label says nothing about. With student, data, optimizer and evaluation fixed, teacher-judged hard negatives and score distillation raise ViDoRe v2 nDCG@5 from 55.2 to 62.6 and 63.0; description alignment, as adapted here, gains 2.6 points and attention grounding nothing measurable. Against teacher-free rules that select four candidates from the same mined pool at identical training compute, the best of which is the positive-aware threshold current systems use, the teacher's judgement adds 4.1 points on v2 and 1.7 on v3. This is consistent with how incomplete the labels are. Annotators judge about two of a query's four top-ranked mined candidates relevant, none of them labelled, so training pushes the retriever away from relevant pages treated as negatives. What reaches the student is coarse: under a greedily decoded 0-100 rating prompt, 82% of the teacher's ratings come back at one end of the scale or the other, and a relevant/irrelevant partition keeps most of the distillation gain. A ten-annotator audit places the teacher within the range of variation among human annotators, and finds it reliable where a query has a single determinate answer. We release the code, the teacher's 3.3M judgements and page descriptions, the mined pools, the human audit and the trained adapters at https://github.com/elastic/vdr-teacher-signals.

cs.IR↗
arXiv2026

PiCA: Pivot-Based Credit Assignment for Search Agentic Reinforcement Learning

Large Language Model (LLM)-based search agents trained with reinforcement learning (RL) have significantly improved the performance of knowledge-intensive tasks. However, existing methods encounter critical challenges in long-horizon credit assignment: (i) Reward Sparsity, where models receive only outcome feedback without step-level guidance to differentiate action quality; (ii) Isolated Credit, where credit is assigned to steps independently, failing to capture sequential dependencies; and (iii) Distributional Shift, where rewards are estimated on templates that deviate from the model's natural generative distribution. To address these issues, we propose Pivot-Based Credit Assignment (PiCA), a novel step reward mechanism that reformulates the search trajectory as a sequential process of cumulative search progress. Unlike prior isolated step rewards, PiCA defines process rewards as success probabilities dependent on the historical context based on Potential-Based Reward Shaping (PBRS). This approach identifies pivot steps, which comprise target golden sub-queries and sub-answers derived from historical trajectories, as information peaks that significantly boost the likelihood of a correct final answer. By anchoring these step rewards to the final task objective, PiCA provides dense, pivot-aware and trajectory-dependent guidance while maintaining distributional consistency. Extensive experiments show that PiCA outperforms existing strong baselines across seven knowledge-intensive QA benchmarks, achieving 15.2% and 4.4% improvements for 3B and 7B models. The consistent performance gains across various models show PiCA's robust generalization. The code is available at https://github.com/novdream/PiCA.

cs.AI↗
arXiv2026

Flow Games with Public Arcs: the Least Core and the Nucleolus

We study flow games with public arcs, an extension of classical cooperative flow games that allows players to use public resources. In these games, a coalition corresponds to a set of arcs, while certain arcs, called public arcs, can be used freely by any coalition. The value of a coalition is the maximum flow value achievable using the arcs controlled by the coalition along with the public arcs. We investigate two solution concepts, the least core and the nucleolus. Both solution concepts provide fair ways to allocate the value of the grand coalition among individual players. We provide polynomial-size formulations of the least core of these games. Besides, we give a polynomial-time algorithm for computing the nucleolus, whether or not the core is empty. This resolves a long-standing gap left by Potters et al. [GEB'06], whose algorithm for the nucleolus of simple flow games with public arcs assumes a non-empty core.

econ.TH↗
arXiv2026

TV-Regulated OPD: Direction Matters in On-Policy Distillation

On-Policy Distillation (OPD) facilitates the transfer of knowledge from domain expert to student in the post-training phase of Large Language Models (LLMs). However, the supervision signals in mainstream OPD methods suffer from high variance and noise which is generally instable during training. In this work, we systematically investigated what really matters to the performance and the fundamental mechanisms behind the instability during training. We found that retaining only the sign of token-level advantages is sufficient to achieve the performance comparable to standard OPD. Meanwhile, smoother and bounded advantages can stabilize the training process without sacrificing its performance. These motivated us to shape the advantages using the Total Variation (TV) and propose a robust TV regulated On-Policy Distillation (TV-OPD) method. Benefiting from the bounded and diminished advantages, TV-OPD exhibits stable training dynamics and steady late-stage performance. We conducted comprehensive experiments and found that, across various settings, TV-OPD consistently achieved better performance and lower variance in the late-stage of training.

cs.LG↗
arXiv2026

Matroid Packing Games: The Core and the Nucleolus

In this paper, we study the matroid base packing problem from the perspective of cooperative game theory and define the associated matroid base packing game. This model extends the network strength game of Baiou and Barahona (2020) from graphic matroids to general matroids. Building on the exchange structure of matroid, we develop a graph theoretic framework for analyzing the core and the nucleolus in this game. This framework yields a compact characterization of core nonemptiness and enables polynomial time algorithms for testing core nonemptiness, finding a core allocation, and computing the nucleolus. In particular, the approach provides a broader and more structured framework for the nucleolus computation problem arising in network strength games studied by Baiou and Barahona (2020), covering both the nonempty core and empty core cases.

cs.GT↗
arXiv2026

TARL: Transaction-Aware Reliable Ledgers for Executable Memory Management in Long-Term Agents

Persistent memory helps long-term agents retain knowledge, yet a single update error can repeatedly distort future retrieval and reasoning. Most existing systems reduce memory updating to a binary Write/Hold decision, which cannot distinguish whether new information should be added, ignored, used to revise an outdated belief, rejected as unreliable, or deferred for verification. These choices may share the same binary label while producing fundamentally different memory states. We introduce TARL, a memory state update framework that maps each statement to one of five executable actions. TARL identifies the affected memory, resolves its temporal scope, compares source reliability, and updates accepted, pending, and rejected ledgers. It is further trained by comparing the memory states produced by alternative update operations, encouraging the model to select the operation that leads to the correct result. We also introduce TARL-Mem, a benchmark with fine-grained action labels and next-state targets. Across in-domain, cross-source, temporal, counterfactual, and sequential evaluations, TARL improves action prediction and state recovery, reduces memory pollution, preserves conflicting evidence, and limits cumulative corruption.

cs.AI↗
arXiv2026

MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research

We present MobileGym, a browser-hosted, lightweight, fully controllable environment for everyday mobile use, targeting interaction fidelity without replicating proprietary backends. It enables two capabilities previously out of reach for everyday apps: verifiable outcome signals through deterministic state-based judging over structured JSON state, and scalable online RL through low-cost parallel rollouts. The full environment state is captured, configured, forked, and compared as structured JSON, and a single server can host hundreds of parallel instances, with about 400 MB memory per instance and about 3 s cold start. A layered state model and a declarative task-definition framework keep state programmability and task creation practical at scale, and a single programmatic judging mechanism delivers both deterministic evaluation verdicts and dense RL rewards. The accompanying MobileGym-Bench provides 416 parameterized task templates, including 256 test and 160 train templates, over 28 apps, with deterministic judges and a structured AnswerSheet protocol that avoids free-text matching failures. In a Sim-to-Real case study, GRPO on Qwen3-VL-4B-Instruct gains +12.8 percentage points on the 256-task test set, and on a 59-task real-device signal subset, real-device execution retains 95.1% of the simulation-side training gain. Project page: https://mobilegym.github.io.

cs.AI↗
arXiv2026

EBRL: Asynchronous Embodied RL by Multi-Grained Resource Management

Embodied reinforcement learning (RL) improves model capabilities with a pipeline of environment simulation, action generation, and model updates. These stages show heterogeneous CPU and GPU demands, making efficient resource utilization difficult. Recent systems overlap rollout (simulation and generation) with training for efficiency, but exclusive GPU allocation and synchronized barrier in rollout still leave substantial hardware resource waste. In this paper, we present EBRL, an asynchronous embodied RL training system with two core techniques. The asynchronous pipelined scheduler overlaps rollout and training, pipelines simulation and generation across environment groups, and carries out each environment independently, eliminating synchronization stalls. The fine-grained resource manager pools CPU cores and GPU streaming multiprocessors, and uses stage profiles and runtime feedback to adjust resource quotas and batch sizes to meet the shifting demands among stages. We implement EBRL on RLinf and evaluate it with four embodied policies and four simulation benchmarks across heterogeneous GPU testbeds. Experiments show that EBRL achieves 1.30-3.47 times the end-to-end rollout throughput and 2.5 times of training convergency compared to the SOTA embodied RL systems.

cs.LG↗
arXiv2026

Reduced-Rank Autoregressive Models for Matrix Time Series

Matrix time series is a series of matrix data observed over time. Analytical tools for such time series is needed in many applications in finance, economics, engineering and many other fields. To avoid the use of vectorization of the matrices which loses the column and row information, and the vector autoregression framework in traditional time series analysis, \cite{chen2021autoregressive} proposed the Matrix Autoregressive (MAR) Model. The model maintains and utilizes the matrix structure, leading to a substantial dimensional reduction and admitting explicit interpretations, comparing with the vector autoregressive model on the vectorized data. However, the MAR model still encounters difficulties in dealing with large dimensional matrix time series as the coefficient matrices in MAR models are also large. In this paper we propose to achieve further dimension reduction through reduced-rank constraints of the coefficient matrices in the MAR model. Estimation and rank determination procedures are studied. Theoretical investigation and empirical examples show that the reduced-rank constraint can achieve higher statistical efficiency than the MAR model.

stat.ME↗
arXiv2026

SeisBench DAS: A machine learning framework for Distributed Acoustic Sensing

Fibre optic sensing, such as distributed acoustic sensing (DAS), has become a widespread technology for geophysical studies. To process the large-scale datasets produced by DAS, several machine learning methods have been proposed. However, without standardization of data and models, these methods lack comparability and interoperability. This introduces a gap between model developers and practitioners analyzing DAS data and inhibits adoption of deep learning for DAS. To address these limitations, here we present SeisBench DAS, an extension to the SeisBench library for machine learning in seismology. SeisBench DAS defines standard formats for DAS benchmark datasets, including standardised metadata and labels, and DAS models. It builds on the xdas framework for data ingestion and virtual array handling, and on PyTorch for reading and applying the machine learning models. Importantly, SeisBench provides an engine to efficiently apply deep learning models to diverse formats of DAS data, bridging the gap between model developers and practitioners. SeisBench DAS is designed as an open and extensible framework, allowing to easily incorporate future developments in deep learning for DAS.

physics.geo-ph↗
arXiv2026

Jina-OCR-v1: Efficient Document Parsing with Speculative Decoding and Dense Verifiable Rewards

We present Jina-OCR-v1, an end-to-end document parsing model built to serve on low-budget GPUs. It combines the compressed-vision encoder and the 3B mixture-of-experts decoder of DeepSeek-OCR, which activates about 570M parameters per token, with a FastMTP speculative decoding head that shares a single draft block recursively across K=3 prediction steps. Greedy verification makes decoding lossless. Post-training combines instruction alignment, robustness fine-tuning on difficult documents, and GRPO under dense verifiable rewards: deterministic formula, table, and structural checks that award partial credit. The training data mixes cleaned public corpora with targeted synthetic pages. At the default dynamic-resolution setting, Jina-OCR-v1 scores 91.14 on OmniDocBench v1.6 and 83.4 on olmOCR-Bench, and reaches the highest page throughput in our comparison at 2.57 pages per second. On a low-budget GPU such as the NVIDIA L4, FastMTP doubles decoding speed over greedy autoregressive decoding. The model is publicly available at https://huggingface.co/jinaai/jina-ocr-v1.

cs.CL↗