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版本更新 · v2arXiv:2503.23013

DAT: Dynamic Alpha Tuning for Hybrid Retrieval in Retrieval-Augmented Generation

Hsin-Ling Hsu, Jerry Wang, Jengnan Tzeng

原文摘要

Hybrid retrieval techniques in Retrieval-Augmented Generation (RAG) systems enhance information retrieval by combining dense and sparse (e.g., BM25-based) retrieval methods. However, existing approaches struggle with adaptability, as fixed weighting schemes fail to adjust to different queries. To address this, we propose DAT (Dynamic Alpha Tuning), a novel hybrid retrieval framework that dynamically balances dense retrieval and BM25 for each query. DAT leverages a large language model (LLM) to evaluate the effectiveness of the top-1 results from both retrieval methods, assigning an effectiveness score to each. It then calibrates the optimal weighting factor through effectiveness score normalization, ensuring a more adaptive and query-aware weighting between the two approaches. Empirical results show that DAT consistently significantly outperforms fixed-weighting hybrid retrieval methods across various evaluation metrics. Even on smaller models, DAT delivers strong performance, highlighting its efficiency and adaptability.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.22307

Exact Gaussian Moment Matching for Residual Networks: a Second-Order Method

Simon Kuang, Xinfan Lin

原文摘要

We study the problem of propagating the mean and covariance of a general multivariate Gaussian distribution through a deep (residual) neural network using layer-by-layer moment matching. We close a longstanding gap by deriving exact moment matching for the probit, GeLU, ReLU (as a limit of GeLU), Heaviside (as a limit of probit), and sine activation functions; for both feedforward and generalized residual layers. On large-variance random networks, median KL-divergence improvements over common baselines range from tens to thousands of times. On a variational Bayes neural network, we show that our method improves upon a state-of-the-art deterministic inference method by an order of magnitude in KL divergence from Monte Carlo ground truth. We also give a smooth-distance error bound showing that, under regularity assumptions, moment matching removes the leading low-variance errors and propagates higher-order local accuracy through the layers of a network.

cs.LG · math.NA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04435

A Systematic Point Cloud Edge Detection Framework for Automatic Aircraft Skin Milling

Zijie Wu, Yaonan Wang, He Xie, Mingtao Feng, Haotian Wu, Chao Ding, Ajmal Mian

原文摘要

The edge detection technique is an essential step for aircraft skin milling in aviation manufacturing. Most of the current detection methods focus on traditionally defined edge extraction tasks but disregard the crucial systematic requirement of edge milling. In this paper, we proposed a novel edge detection framework for automatic edge milling of aircraft skins. First, an edge probability detector is proposed by the spatial tangent continuity (STC) to provide the essential reference. Second, we propose a hierarchical branch searching (HBS) method to hierarchically strip the desired milling edges from the raw point cloud, which consists of three graded progressive steps: branch backbone generation, branch extension, and branch pruning. We demonstrate the performance of the proposed method on both synthetic models and aircraft skin workpieces. The proposed method outperforms the other baselines and shows accurate edges for the edge milling task.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04434

Sketchpad: certified exact arithmetic with a small vision-language model

Mostafa 'Neo' Mohsenvand

原文摘要

Language models are accurate on most long-arithmetic problems, but their accuracy is estimated on samples and carries no guarantee for an unseen input; tool use is exact but moves the computation outside the network. We show that exactness at all input lengths becomes a finite, checkable property of a network when each of its steps reads only a bounded view of an external working memory. In Sketchpad, Qwen3.5-0.8B with a low-rank adapter executes arithmetic procedures on a rendered sheet. Each forward pass reads a three-cell view and emits one step: a digit, a carry or borrow, and the next operation. A fixed runtime moves the view and writes no digit of a result. We prove in Lean that the model is then exact on all inputs of any length if and only if it is exact on the finite set of views the runtime presents, that no finite test suffices when steps also read earlier steps, and that the guarantee tolerates bounded numerical perturbation and extends to compositions of procedures. All 2,446 views pass on the deployment device, a laptop GPU running the model in a web browser. The certified model performs signed addition and subtraction, long multiplication and division, square roots and greatest common divisors at arbitrary length. It solved 200 of 200 pre-registered problems in the browser, and 220 of 220 sums of up to 1,000 digits and products of up to 20 digits, of which GPT-6.1 Sol, the strongest of ten other models, solved 217. A symbolic runtime built on these procedures solves, expands and factors polynomials with every number computed by the model, and a multiplicative gate on the adapter recovers the base model exactly when the sheet is not in use.

cs.LG · cs.CL · cs.LO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2512.17220

Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understanding

Yuqing Li, Jiangnan Li, Zheng Lin, Ziyan Zhou, Junjie Wu, Jie Zhou, Mo Yu

原文摘要

Humans understand long and complex texts by relying on a holistic semantic representation of the content. This global view helps organize prior knowledge, interpret new information, and integrate evidence dispersed across a document, as revealed by the Mindscape-Aware Capability of humans in psychology. Current Retrieval-Augmented Generation (RAG) systems lack such guidance and therefore struggle with long-context tasks. In this paper, we propose Mindscape-Aware RAG (MiA-RAG), the first framework to formulate mindscape-aware retrieval and generation as a unified conditioning paradigm for LLM-based RAG. MiA-RAG builds a mindscape through hierarchical summarization and conditions both retrieval and generation on this global semantic representation. This enables the retriever to form enriched query embeddings and the generator to reason over retrieved evidence within a coherent global context. We evaluate MiA-RAG across diverse long-context and bilingual benchmarks for evidence-based understanding and global sense-making. It consistently surpasses baselines, and further analysis shows that it aligns local details with a coherent global representation, enabling more human-like long-context retrieval and reasoning.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02629

DexArena: A Unified Benchmark for Long-Horizon Dexterous Manipulation

Haonan Ping, Jianshu Hu, Yimin Wang, Yi Zhu, Pai Liang, Qiyan Jin, Qirui Bao, Ying Qian 等 15 位作者

原文摘要

Long-horizon dexterous manipulation remains a fundamental challenge for robot learning, requiring the composition of multiple skills, precise bimanual coordination, generalizable visual understanding, and robust skill transitions. Yet existing manipulation benchmarks largely emphasize isolated skills or support only a limited range of end-effectors, making these capabilities difficult to evaluate systematically. We introduce DexArena, a benchmark designed to evaluate learning and generalization in long-horizon bimanual dexterous manipulation. DexArena features 10 long-horizon tasks and 30 stage-level tasks spanning diverse household and industrial scenarios. Each long-horizon task is composed of explicitly defined stages with physically grounded success conditions, enabling reliable evaluation of both intermediate progress and end-to-end task completion. A unified benchmark interface and verified human-teleoperated demonstrations enable testing different policy learning algorithms, with standardized simulation configurations, scene randomization, trajectory replay, and task-level evaluation. Through extensive baseline experiments, we identify substantial challenges in precise manipulation, generalization to scene variation, and skill composition. DexArena establishes a challenging and reproducible benchmark for developing robot learning methods that move toward generalizable long-horizon dexterous behavior.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05430

On the most informative Boolean function under noise

Dimiter Kramer, Mert Saglam

原文摘要

Let $X$ be a uniform random bit string and $Y$ be obtained by flipping each bit of $X$ independently with probability $p$. Let $C$ be a random bit obtained as a function of $Y$. We show that $\mathbf{I}(C:X)\le \mathbf{I}(Y_1:X)$ for all $p\in[0,1]$, which resolves a conjecture posed by Courtade and Kumar in 2013.

cs.IT · math.CO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04425

Minimizing Bottleneck Overload Risk in Online Edge Task Placement under Workload Uncertainty

Lei Wang, Winston K. G. Seah, Gang Xu, Xiaodong Xu, Baoqi Huang

原文摘要

In edge computing, tasks must be placed on servers of finite capacity before workloads are realized, often with only workload means and variances known. An assignment balanced on expected load can still leave a server with more fluctuations than its residual capacity can absorb. Per-server risk constraints can enforce a prescribed overload tolerance but do not distinguish the assignments that meet it by how close the riskiest server comes to the limit. We show that, for independent tasks, placement preserves a period's total variance and total residual capacity, and decides only how fluctuation, measured by standard deviation, is paired with residual capacity across servers. The risk ratio, the ratio of fluctuation to residual capacity, parameterizes a server's worst-case overload probability over all aggregate workload distributions with given mean and variance. We minimize the largest risk ratio, attained at the risk bottleneck. Risk-Bottleneck Placement (RBP) places tasks in descending order of standard deviation, each on the server with the smallest post-assignment risk ratio, and refines the assignment with relocations and swaps within a finite per-period online budget. On small instances solved exactly by enumerating every assignment, minimizing the maximum expected utilization leaves the largest risk ratio 25.66-29.19% above its exact minimum on average, even with risk-minimizing tie-breaking, and the corresponding gap for RBP is 1.77-2.64%. In trace-derived multi-period simulations at two workload pressures, RBP reduces the worst-server overload probability by 26.0% and 18.0% relative to the best baseline and remains the lowest at every tested pressure and scale.

cs.DC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.08586

MINDSET: Energy-based Memory Evolution for Long Conversational Agents

Sujato Dutta, Sreekruthy Tummala, Shashank Vanga, Ayushmi Pavani, Neeraj Choudhary

原文摘要

In conversational memory, a later statement may replace an earlier fact, describe a different context, or be an isolated contradiction. Thus, agentic memory must track what changed over time without erasing what was true before. We introduce MINDSET, a memory controller that makes this distinction through explicit state transitions. It preserves original dialogue episodes and organizes them into versioned schemas. For each new episode, it chooses whether to reinforce a schema, supersede its current state, create a contextual branch, or start a new schema. A local energy score balances coherence, contradiction, historical preservation and fragmentation. Stability rules prevent isolated conflicts from immediately replacing established memory. On a frozen 700-question LoCoMo subset, MINDSET achieves the highest observed answer F1 among six evaluated systems and significantly improves retrieval ranking over LightMem. Without retuning, it obtains 69.23% judge accuracy on all LongMemEval knowledge-update cases, compared with 64.10% for Vector RAG. Two additional answer model backbones using Gemma-4-31B and GLM-4.7 provide further qualified evidence of generalization. These findings support explicit memory-state management as a useful approach to changing conversational knowledge.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.27304

The Power of Recruiting the Smaller Side: Two Additional Traders Suffice in Two-Sided Markets

Yang Cai, Vineet Gupta, Yanchen Jiang, Christopher Liaw, Aranyak Mehta, Grigoris Velegkas, Di Wang, Mingfei Zhao

原文摘要

We study Bulow-Klemperer-style competition complexity in two-sided double auctions with $m$ unit-demand buyers drawn i.i.d. from $F_B$ and $n$ unit-supply sellers drawn i.i.d. from $F_S$. When $m \ge n$ and buyer valuations first-order stochastically dominate seller costs ($F_B \succeq_{\mathrm{FSD}} F_S$), we prove that recruiting just two additional sellers enables Seller Trade Reduction (STR), a prior-independent mechanism, to achieve expected Gains From Trade (GFT) at least the first-best GFT of the original market; symmetrically, when $m \le n$, Buyer Trade Reduction suffices with two additional buyers. This resolves open questions of Babaioff, Goldner, and Gonczarowski (SODA 2020) and Cai, Liaw, Mehta, and Zhao (STOC 2024). Moreover, this uniform bound is optimal: already for $m = n = 1$, no prior-independent mechanism (deterministic or randomized) that is dominant-strategy incentive-compatible (DSIC), individually rational (IR), and weakly budget-balanced (WBB) matches the first-best GFT with one additional seller. We further extend our characterization to $r$-dominance ($F_B \succeq_r F_S$), where $r \in (0, 1]$ and $1 - F_B(x) \ge r(1 - F_S(x))$ for all $x \in \mathbb{R}$ (recovering $F_B \succeq_{\mathrm{FSD}} F_S$ at $r = 1$). When $m \ge n \ge 1$, $c = \lceil 1/r \rceil + 1$ additional sellers suffice for STR to match or exceed the first-best GFT, while every prior-independent DSIC, IR, and WBB mechanism requires at least $\max(2, \lceil 1/r \rceil - 1)$ additional sellers even for $m = n = 1$, pinning down the optimal bound to within two traders. Finally, this $Θ(1/r)$ bound cannot be asymptotically improved even by recruiting on both sides: for $r < 1/64$ and $m = n = \lceil 2/(5r) \rceil$, every prior-independent DSIC, IR, and WBB mechanism requires $c \ge \lceil 1/(64r) \rceil = Ω(1/r)$ additional traders per side.

cs.GT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.23711

A Fixed-Parameter Algorithm for 4-Block Integer Programming

Klaus Jansen, Felix Ohnesorge, Corinna Wambsganz

原文摘要

We give a fixed-parameter tractable (FPT) algorithm with running time $f(k,\barΔ)\cdot {|I|}^{O(1)}$ for integer linear programs with 4-block structure, parameterized by the maximum block dimension $k$ and the largest absolute entry $\barΔ$ in the local-column blocks. This result resolves a long-standing open question in parameterized complexity, and answers a conjecture by Eisenbrand and Rothvoss (2026) in the positive. This result has several implications for other block-structured integer programming models, including mixed fracture number and special cases of 4-block programs with large entries outside of the diagonal. Important tools for this algorithm are structural properties of generalized $n$-fold integer programs shown by Ligthart~(2026) and an algorithm by Veselov, Gribanov, Zolotykh and Chirkov~(2020) for optimizing discrete convic functions. Independently and shortly after the first version of our paper appeared, Koutecký, Lassota, and Ligthart (2026) obtained an FPT algorithm for 4-block integer programming.

cs.CC · cs.DS

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.22547

When Pass@$k$ Crossovers Are Real - and Why: Paired Inference and the Markov Kernel of RLVR

Chen Yang

原文摘要

Reinforcement learning with verifiable rewards (RLVR) typically improves pass@1 but can fall behind its base model at large sampling budgets $k$, commonly interpreted as sharpening existing solutions. Because adjacent pass@$k$ estimates reuse generations, pointwise error bars cannot establish a crossover. We formulate paired inference on the RL-minus-base difference curve: a prompt-level multiplier bootstrap yields a one-sided dominance test and simultaneous confidence bands requiring both an early gain and a later loss. Among nine public RLVR checkpoints at 3k-16k token limits, only one narrowly meets this criterion before multiplicity adjustment; asymmetric truncation can create, mask, or reverse crossovers. On 1,060 deduplicated competition problems at 32k tokens, two 1.5B models exhibit certified crossovers around $k\approx10$-$60$, while two 7B models show no supported loss. A paired variance decomposition shows why adding prompts improves power more than deeper sampling. Prompts with identical base success probability diverge after RLVR, with dispersion replicating across independent generation halves and violating Lipschitz slope bounds for reasonable deterministic maps. Modeling RLVR as a Markov kernel, Jensen's inequality shows that conditional dispersion depresses pass@$k$ for $k\ge2$. Once dispersion is modeled, the conditional-mean log-odds slope is below one in every pair: RLVR improves hard prompts on average, while late-$k$ loss comes from a 2%-3% minority of base-solved prompts collapsing onto a repeated modal wrong answer and resisting prefix rescue up to 4,096 tokens. In controlled runs from one base model on 3,000 new problems, all four GRPO arms fall behind near $k\approx200$ and remain behind through $k=4096$, while SFT stays significantly ahead throughout.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04424

ReflexArc: Bottom-Up Integration of Instantaneous Tactile Reflexes into Whole-Body Humanoid Control

Mingyu Wang, Si Li, Ziqing Chen, He Wang, Li Yi

原文摘要

Humanoid robots operating in cluttered environments must react to unexpected contacts before the first impact peak, which arrives within tens of milliseconds. Learned whole-body policies running at 50 Hz introduce a policy-cycle delay and may suppress a protective withdrawal as a tracking error. To address these challenges, we propose ARC (Ascending Reflex Control), a bottom-up framework that integrates instantaneous tactile reflexes with learned whole-body control. ARC consists of two components: an instantaneous reflex pathway and bottom-up reflex integration. The reflex pathway detects hazardous contacts from full-body tactile sensing, solves a whole-body withdrawal pose, and delivers it directly to the motors within 10 ms. Bottom-up reflex integration combines reflex-in-the-loop policy learning, reflex-transparent policy alignment, and reflex-informed policy adaptation to sustain the withdrawal, maintain balance, and complete longer-horizon recovery. We deploy ARC on a Unitree G1 and evaluate three categories of hazardous contact. Compared with an otherwise identical reflex delayed by 20 ms, ARC reduces the first force peak by 34.1% and reduces the contact impulse from 0.2632 N s to 0.1225 N s (a 2.15x ratio). We further demonstrate protected motion tracking, teleoperation, and vision-free obstacle traversal guided by foot tactile sensing. Project page: https://minusmodulo.github.io/reflexarc-website/

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04423

SWAP-INR: Implicit Neural Representation with Switchable Attention Paths for Time-varying Multivariate Scientific Data Compression

Hyunsoo Son, Jeonghyun Noh, Chaoli Wang, Won-Ki Jeong

原文摘要

Visualizing multivariate scientific simulation data is essential for understanding complex physical phenomena, yet the massive scale of such data imposes a severe storage bottleneck in high-performance computing environments. Implicit neural representations (INRs) have emerged as a promising continuous alternative to traditional lossy compressors, but existing methods struggle to explicitly capture cross-variable interactions and to adapt to the diverse spectral characteristics of physical variables. To address these limitations, we propose SWAP-INR, an architecture for high-fidelity compression of multivariate, time-varying simulation data. SWAP-INR introduces two key components: (1) The switchable attention path (SWAP) module explicitly models cross-variable interactions through a cross-variable attention layer, then routes each variable to an attention or non-attention path by a deterministic, loss-driven criterion, and prunes the inactive layers for structural compression. (2) The adaptability amplification SIREN (A2-SIREN) replaces rigid frequency scaling with channel-wise learnable modulation, adapting to heterogeneous physical features with negligible overhead. Combined with post-training 16-bit quantization, SWAP-INR achieves state-of-the-art performance on small modular reactor (SMR) datasets and remains competitive on public physics simulation datasets, while maintaining a constrained memory footprint. Our code is https://github.com/hvcl/SWAP-INR.

cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.15173

Time-Deterministic and Instruction-Dynamic Execution: A field-programmable gate array quantum-control processor

Xiaoqin Luo, Jiayun Song, Xiaolu Su

原文摘要

Long-running quantum computations, including error-corrected workloads, demand instruction availability, measurement-dependent control, and calibration updates. We present Time-Deterministic and Instruction-Dynamic Execution, an FPGA quantum-control processor supporting runtime revision at both instruction and parameter levels. Dynamic Instruction Parameter Update supplies one-shot, next-match patches before parameter capture, while Dynamic Instruction Stream Overwrite provides guarded replacement, logical deletion, and out-of-line insertion in eligible future regions of a resident program. A moving fetch-visibility guard coordinates instruction updates with execution, and event commitment isolates complete descriptors for independent hardware-timed dispatch. Revision applicability depends on update timing and execution progress; deterministic service applies to committed events under the stated scheduling conditions. The Xilinx ZCU102 implementation uses a 250-MHz control core and a 425-MHz timing/update domain. FPGA-resident measurements at the registered digital interfaces confirmed programmed-cycle dispatch for all tested descriptors committed at least one timing-domain cycle before their timestamps, with downstream ready and the stated FIFO ordering conditions. Separate tests confirmed post-commit timestamp and payload invariance. The minimum all-success mapped DIPU margin was four control-domain cycles, and a replacement of L instruction words completed in L+5 update-domain cycles under continuous payload delivery. Within the tested guard-distance range, rejected DISO requests reported an error and preserved the resident path, whereas all admitted revisions tested here executed complete revised sequences. These results provide a hardware execution basis for incorporating runtime-generated sequences and parameter corrections into precisely timed quantum computations with feedback.

quant-ph · cs.AR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04422

GVT3D: Gesture-Vision-Text Token Enrichment for 3D Embodied Reference Understanding

Vimarsh Shah, Dulanga Weerakoon, Ajinkya Deshpande, Saksham Agarwal, Nilesh Bhatia, Neel Kiran Naik, Parth Shah, Sravan Danda 等 12 位作者

原文摘要

Natural human-robot interaction requires robots to interpret language and gestures within a 3D environment to support tasks such as pick-and-place and object retrieval. 3D Embodied Reference Understanding (3D-ERU) supports this by identifying the 3D location of a target object from a scene (point-cloud) containing a human pointing gesture and an accompanying language description. However, current 3D-ERU models lack alignment of cross-modal representations and incur high processing costs that increase with the point-cloud's size. To address this, we propose GVT3D, which employs an Embodied Fusion module that jointly models language, gesture, and vision inputs within a shared embedding space, promoting cross-modal alignment. We additionally introduce a foveation approach that selectively processes the point cloud within a coarse region around the estimated pointing direction to reduce computations. GVT3D achieves ~8% higher accuracy and foveation enables 2.95x faster execution while maintaining comparable accuracy, relative to prior 3D-ERU models.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.39861

M-PFT: Angular Margin for Calibrated Prompt-Tuning of Fine-Tuned Vision-Language Models

Shihab Aaqil Ahamed, Mohamed Ansaff, Nang Khin Myat Lay Phyu, Udaya Sampath K. Perera Miriya Thanthrige, Chamira U. S. Edussooriya, Ranga Rodrigo, Muhammad Haris Khan

原文摘要

Prompt-tuning has emerged as a parameter-efficient technique for adapting large-scale vision-language models (VLMs) to downstream tasks while keeping the pre-trained weights frozen. However, prompt-tuned VLMs often suffer from a base-to-novel dual miscalibration problem, where models typically show underconfidence on base classes and overconfidence on novel classes. Current VLM calibration approaches address this dual miscalibration either separately or jointly, but lack a geometric understanding of the hyperspherical embedding space and thus fail to exploit angular discriminative properties to adjust distorted decision boundaries. To address this, we propose M-PFT, a novel angular-margin approach for prompt tuning that introduces two complementary objectives to promote class discriminability and semantic anchoring. This enlarges the angular margin by 1) minimizing the maximum cosine similarity between tunable class embeddings to reduce embedding collapse and 2) minimizing the angular distance between each tunable embedding and its corresponding zero-shot embedding to preserve semantic geometry. Extensive experiments across 9 prompt-tuning methods with various backbones on 15 diverse datasets show that our approach reduces the average calibration error on novel classes, while remaining competitive on base classes and maintaining comparable accuracy. Further analyses reveal that M-PFT effectively adjusts distorted decision boundaries induced by prompt tuning while preserving the pre-trained embedding space, improving calibration with negligible computational overhead.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04421

Typological Feature Induction as a Benchmark for Inductive Metalinguistic Reasoning

Chen Zhang, Seth Aycock, Yusuke Miyao

原文摘要

Existing metalinguistic reasoning benchmarks require both inference of rules and their application, making failures hard to diagnose. We introduce typological feature induction (TFI) which targets the former step. Given sentences in an unnamed language paired with translations in a known pivot language, models must predict a typological feature of the unnamed language. From Grambank and multi-parallel corpora, we automatically build evaluation questions over 195 languages. We find that LLMs identify 59--80% of these languages from only 16 sentence pairs; and misaligning the translations barely affects their accuracy, suggesting reliance on prior knowledge of the language. Enciphering text to prevent recognition increases the corpus' utility, but mainly for features where target and pivot languages agree. On languages which models cannot identify, neither open model outperforms a majority-value baseline. Since induction from raw text aligns with field linguists' use-case, this work indicates the models tested are not yet ready for this role. TFI offers a scalable path to track progress in this direction.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.29734

HTAM: Hierarchical Transition-Attended Memory for Operator Optimization

Yining Zhang, Mingyang Yi, Chen Wang, Xuwen Xiang, Tianhe Jia, Zedong Dan, Chengqing Zong, Yue Wang

原文摘要

High-performance GPU kernels are essential for efficient LLM deployment, yet optimizing them remains expertise-intensive. Recent LLM-based code generation makes automatic GPU operator generation promising, but operator optimization remains a hardware-aware search problem. Existing LLM-based methods face a granularity mismatch: coarse hints are reusable but hard to execute, whereas detailed memories are actionable but enlarge the search space and obscure optimization bottlenecks. The key challenge is therefore to organize optimization experience at an appropriate granularity. To address this issue, this paper proposes HTAM (Hierarchical Transition-Attended Memory), a coarse-to-fine framework for LLM-based operator optimization. HTAM builds a two-level Hierarchical Transition Graph (HTG) to organize coarse global directions, detailed local strategies, and transition experience between optimization steps. During each evolution step, HTAM selects a global direction from the current state and recent optimization history, retrieves the corresponding local strategy memory, and uses it to guide concrete CUDA code generation. Experiments on KernelBench demonstrate that HTAM improves correctness, fast-solution rate, and speedup over LLM-based baselines, while backend and Robust-KBench studies indicate transferable benefits from structured memory.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04420

Verifier strength does not buy discovery quality: a pre-registered two-domain test of LLM-based automated research, with post-hoc evidence pointing to proposer-pool composition

Peter Chen, Ni Zhang, Siyuan Zhang

原文摘要

Automated research pipelines pair an LLM proposer with a verifier on an implicit bet: discovery quality rises with verification strength. Under a pre-registered protocol we test this bet in two real-data domains -- job-shop scheduling and vehicular edge offloading on Beijing T-Drive trajectories -- across 100 matched LLM/random pairs per domain and three verifier tiers. The manipulation binds -- a 429-case mutation battery shows scores rising across tiers -- yet the paired LLM-over-random advantage does not scale with it (A, one-sided p=0.1115; D, p=0.9994), and in D shrinks under the strict tier (margin contrast -0.040, p=0.0013). The two lower gates promote identical candidates in 100/100 and 99/100 pairs, so the strict order is untestable when they coincide. Constructively, LLM proposals carry a real paired advantage over random controls, but a post-hoc union-pool analysis shows the fastest path to better discovery is widening the pool, not tightening the gate: mixing the two pools adds +1.52 holdout-value units in domain A (one-sided Wilcoxon, n=100, p=0.0002) -- evidence that pool composition, not verification strength, is the lever. Criteria, ledgers and scripts are archived on Zenodo (DOI: 10.5281/zenodo.23308866).

cs.AI · cs.SE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.21375

Memorilla: Latent Semantic Memory for LLMs

Adibvafa Fallahpour, Parsa Idehpour, Vignesh Kothapalli, Nikita Mounier, Shirley Wu, Shayan Pardis, Jure Leskovec

原文摘要

Memory enables large language models (LLMs) to use information beyond their parameters, such as past user conversations, long documents, or an agent's earlier actions. For a frozen LLM, access to this accumulated knowledge is limited to what a memory system places in its context window. However, gathering and combining relevant information needed for each query while keeping the model's context short remains challenging for existing systems. Here we introduce Memorilla, a memory system that gathers information from an entire document collection into a fixed number of query-dependent 'memory tokens' for a frozen LLM. Memorilla encodes documents (such as user chats, book chapters) once into reusable embeddings, then learns to retrieve and place query-specific information in the LLM's input embedding space. Using only 16 memory tokens, Memorilla achieves the best performance against baselines including Mem0 and MemoRAG on five of eight benchmarks spanning personalization, long-document and knowledge-intensive question answering. Notably, on FactKG, it reaches 84.33% accuracy versus 80.43% with full-context inference, while using 108x fewer tokens to represent the source documents. Furthermore, Memorilla can facilitate continual learning in agentic systems where the document count (the agent's past turns) increases throughout the trajectory. In a TextWorld environment, we show that Memorilla reduces the agent's tendency to wander between rooms it has already explored, and improves its average success rate from 18.6% with full-history in-context learning to 24.2%. Overall, Memorilla aids frozen LLMs in accumulating and using knowledge effectively across long-context interactions and takes a step towards building continually learning AI systems.

cs.LG · cs.AI

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新论文 · v1arXiv:2611.04418

VidUEU-Agent: A Data-Curation Agent for Multimodal Understanding, Editing, and Unified Tasks

Tengju Ru, Weitong Lian, Zecong Tang, Lingyi Meng, Haoran Li, Zhejun Cui, Yichen Zhu, Hangshuo Cao 等 15 位作者

原文摘要

The advancement of multimodal learning is heavily constrained by the scarcity and high annotation cost of high-quality instruction data. While raw videos offer an abundant and dynamic source of visual knowledge, transforming them into precise instruction data remains highly challenging. To bridge this gap, we present VidUEU-Agent, an automated pipeline that synthesizes instruction data from raw videos for multimodal understanding, editing, and unified tasks. VidUEU-Agent follows a four-stage pipeline: it performs global perception to filter low-quality shots, mines keyframes and multimodal signals, constructs metadata and routes the metadata to synthesize instruction samples. More importantly, our agent framework can autonomously iterate the data distribution based on downstream training results, thereby optimizing data quality, balance, and training effectiveness. Scaling this pipeline, we construct VidUEU-100K, a large-scale dataset of 100K training samples spanning multiple domains and tasks, providing a robust foundation for developing comprehensive multimodal capabilities. Extensive experiments demonstrate that fine-tuning baseline models on VidUEU-100K yields significant and consistent performance gains across diverse representative benchmarks, validating the effectiveness of our proposed agent framework and dataset.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.35541

The Agent in the Middle: How AI-Mediated Access Reshapes Web Vulnerability Exploitation

Ruozhao Yang, Jiongchi Yu, Ming Hu, Xiaofei Xie

原文摘要

Web services increasingly expose backend interfaces that let users delegate tasks to AI agents, which invoke service operations directly without navigating websites. This shift changes how existing Web vulnerabilities can be exploited. We investigate these changes through VELA, an executable benchmark covering 44 vulnerabilities across twelve Web application families. The benchmark preserves vulnerable backends and derives agent-facing interfaces from legitimate workflows. We evaluate three commercial agents across multiple interface configurations, using controlled comparisons to separate interface effects from agent behavior and independent oracles to verify attack outcomes. Our results show that the agent realizes every attack that direct interface invocation realizes in the reference configuration, so exploitability is set mainly by the interface: 91% of vulnerabilities remain exploitable under the reference design. Interface restrictions prevent some attacks by removing required operations or limiting input control, while retention remains high across vulnerability classes. No case-level loss in the reference configuration is attributed to agent refusal or input normalization, and in one case the agent completed an attack by supplying calls the attacker omitted. Across the tested configurations, interface design affects exploitability more than agent choice, moving retention from 39% to 95% against at most 4.5 points across agents, but some restrictive interfaces also limit legitimate workflows. Our study provides a systematic empirical account of how agent-mediated access reshapes existing Web vulnerabilities, grounding future Web architecture design in measured security outcomes and trade-offs between reducing attack exposure and preserving legitimate functionality in agent-facing services.

cs.CR · cs.AI · cs.SE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.29167

JDCNet Uses Confidence Gated Privileged Modality Distillation for Cost Preserving X Ray Inference

Bo Ma, Weiqi Yan, Jinsong Wu, Kun Liu

原文摘要

Training-only privileged-modality learning uses an expensive modality during training while keeping a single-modality deployment path. We study chest radiograph (X-ray) classification with same-patient computed tomography (CT) available only during training. JDCNet adds an auxiliary CT-teacher target, the teacher's argmax label or its softmax distribution through a Kullback--Leibler term (soft-KL), only on samples whose teacher confidence exceeds a threshold; the deployed model has the same ResNet-18 computation graph as the supervised baseline. On 510 same-patient pairs from the public BIMCV-COVID19 releases of the Valencian Region Medical ImageBank (113 COVID-19, 397 non-COVID), with patient-level five-fold cross-validation and three seeds, 15 of 16 adaptively explored JDCNet configurations improved mean balanced accuracy (BA) over the supervised baseline (average $+0.020$), but none remained significant after Holm adjustment. In a pre-registered experiment with 240 batch-matched supervised restarts on the same splits, batch size had no detectable effect on the baseline (BA $0.620$ versus $0.625$), and when both methods selected one of 16 runs per cell on the validation partition, JDCNet exceeded supervised training by $+0.037$ BA (95\% confidence interval $+0.016$ to $+0.058$; patient-level $+0.007$ to $+0.068$). Logit distillation and contrastive alignment did not help. Teacher diagnostics indicate that the gate acts mainly as CT-informed sample weighting. All models, including JDCNet, performed at chance on an external radiograph set. Confidence-gated CT supervision thus gives a small within-cohort advantage on one public cohort and a binary task; generalization requires independent paired replication.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04416

Efficient and Faithful High-Resolution Image Editing with Sparse Cross-Resolution Denoising

Maosen Zhao, Xianfang Zeng, Pengtao Chen, Peng Ye, Gang Yu, Jiayuan Fan, Tao Chen

原文摘要

Instruction-based image editors still primarily operate at 1K, whereas practical editing increasingly requires faithful 2K/4K outputs. Dense high-resolution adaptation preserves the editing trajectory but incurs expensive target-source attention; editing at 1K followed by external refinement is cheaper but can separate detail restoration from the editing trajectory. We introduce FLASH, a native high-resolution editor based on sparse cross-resolution denoising. A frozen 1K branch provides a semantic and structural anchor, while a target-resolution branch reconstructs local detail within the same Transformer. An asymmetric topology retains full 1K attention, guides high-resolution queries with spatially aligned low-resolution noisy-target states, and confines high-resolution target-reference interaction to local Halo neighborhoods. Resolution-specific diffusion-state mapping and spatial YaRN adapt the pretrained editor to larger grids. An optional, training-free progressive path replaces early high-resolution denoising with a 1K prefix and completes refinement in the same editor. On VINS-4KEval, the direct model achieves the strongest structural fidelity among the compared methods at both 2K and 4K. A separate 4K forward benchmark with R = 1 records 4.2x faster total forward execution and 25.3x faster attention than dense target-reference attention. Progressive inference reduces end-to-end 4K latency by 30.5%, with a modest quality cost.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2605.06046

Requests of a Feather Must Flock Together: Batch Size vs. Prefix Homogeneity in LLM Inference

Saksham Rathi, Preeti, Mythili Vutukuru

原文摘要

Auto-regressive token generation in large language models is memory-bound because it requires "attending to" key and value tensors (KV cache) of all previous tokens. Prior work aims to improve the efficiency of this decode process by batching multiple requests together, and maximizing batch size subject to GPU memory constraints. The key observation of our work is that with prefix-sharing workloads, smaller, prefix-homogeneous batches - where all requests share a common prefix - can achieve higher decode throughput than larger, heterogeneous batches, due to better spatial and temporal locality during KV cache accesses. However, current prefix-aware schedulers maximize prefix reuse within a batch only to reduce KV cache memory footprint, but do not stop batch formation at smaller homogeneous batches that could have performed better. Further, we show that shared prefix detection in existing schedulers relies on radix-tree traversals, incurring substantial CPU overhead that is often comparable to GPU execution time. This paper presents Feather, a prefix-aware scheduler that uses reinforcement learning (RL) to learn the optimal tradeoff between batch size and prefix homogeneity. We also introduce Chunked Hash Tree (CHT), a lightweight data structure that enables fast prefix detection and efficient request selection for the RL scheduler, avoiding expensive tree traversals. We integrate Feather into vLLM and SGLang, and our evaluation shows that Feather achieves 2-10$\times$ higher end-to-end throughput as compared to existing schedulers by reducing the total number of KV cache accesses, while doing no worse than the status quo when there is insufficient prefix sharing.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.18410

MAGDrop: Momentum Adaptive Gradient Dropout With Provable Generalization Guarantees

Adeel Safder, Bum Jun Kim

原文摘要

Standard dropout applies a fixed, uniform noise rate to all units, ignoring available gradient information. We propose momentum adaptive gradient dropout (MAGDrop), a Bayesian linear layer that adapts per-unit weight noise by tracking an exponential moving average of gradient magnitudes. Units with large recent gradient magnitudes receive less noise to preserve learned features; units with below-average gradient magnitudes receive more noise to promote exploration. This adaptation transforms the classifier head into a data-dependent Bayesian predictor whose mean predictor has no additional inference cost. Our primary contribution is a certificate-aware low-rank posterior, MAGDrop-Subspace, that addresses the tension between strong accuracy and tight bounds from probably approximately correct (PAC)-Bayes theory. A full-head stochastic posterior incurs a Kullback--Leibler (KL) divergence of approximately 8,625 nats. By confining the posterior to an $r$-dimensional subspace constructed from an independent prior-construction sample $S_0$ and certifying on an independent sample $S$ with $S_0\perp S$, MAGDrop-Subspace reduces KL by approximately a factor of 800 while keeping accuracy close to the baseline. On CIFAR-10 with a 13-layer convolutional neural network and $r=50$, MAGDrop-Subspace achieves a PAC-Bayes Gibbs-risk bound of 0.1882 at 90\% confidence using binary KL inversion, compared with 0.4439 for the full-head baseline. Mean-predictor accuracy is 82.31\%, compared with 83.74\% for the baseline. Complementary accuracy experiments on ImageNet-1K show that MAGDrop reaches top-1 accuracy similar to the strong baseline: 78.57\% compared with 78.51\% on the base Vision Transformer with $16\times16$ patches, and 82.14\% compared with 82.23\% on ConvNeXt-Base.

cs.LG · math.ST

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新论文 · v1arXiv:2611.04414

SphereFlow: Missing Modality Imputation via Geometric Transport on Hypersphere

Huihan Wang, Juncheng Wang, Yuxiang Feng, Wenlong Hou, Guoqi Yu, Chao Xu, Yang Liu, Baigui Sun 等 9 位作者

原文摘要

Missing modality imputation aims to synthesize unobserved modalities from observed ones. Existing methods treat each modality as living on its own latent manifold, forcing the generator to learn a long, unstructured jump between them. We present SphereFlow, which takes a fundamentally different view: by projecting all modalities into a shared hyperspherical latent space via a frozen self-supervised encoder, cross-modal imputation reduces to short-range geometric transport between nearby points on the sphere. Realizing this idea with flow matching, however, exposes two geometric conflicts: the standard Gaussian noise source is catastrophically mismatched with the unit-norm data manifold, and the linear interpolation path departs the sphere surface into a region where the decoder has no training signal. We resolve both with simple, geometry-aware modifications: replacing the noise source with the observed-modality latent so that the velocity field learns only the cross-modal displacement, and training the decoder with a time-weighted loss that extends its domain to the near-surface region traversed by the flow. We prove that this data-as-source formulation reduces the worst-case off-manifold deviation from a quantity that grows with the latent dimension to a small, dimension-independent bound governed by inter-modality similarity. Experiments on BraTS multi-modal MRI imputation and CT-MRI translation demonstrate state-of-the-art generation fidelity across all settings, while downstream brain tumor segmentation with our imputed data closes roughly eighty percent of the gap to the full-modality oracle. Code is available at https://github.com/WTHH031230/SphereFlow.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04413

CaliSplat: Sparse Gaussian Fitting and Calibrated Rate Allocation for Image Compression

Zhenbang Zhang, Zhengquan Luo, Kai Zhao, Zhiqiang Xu

原文摘要

2D Gaussian representations provide an explicit and lightweight alternative for image representation, enabling fast deterministic decoding without a heavy neural synthesis network. However, existing per-image Gaussian codecs still require costly image-specific optimization to determine how limited Gaussian capacity should be distributed across heterogeneous regions. We introduce CaliSplat, an optimization-efficient Gaussian image codec that selectively explores candidate representations and globally allocates a constrained Gaussian budget. We exploit the closed-form reconstruction error of the single-Gaussian case as an inexpensive measure of local difficulty, fitting higher-capacity candidates only in regions where additional Gaussians are likely to be useful. To reduce selection errors caused by mismatched convergence rates across candidate sizes, a small set of longer-horizon fits is used to calibrate short-horizon distortion estimates before global budget-aware selection. The selected representation is then compressed into a bitstream. Across standard and large image datasets, CaliSplat achieves competitive or superior rate--distortion performance while reducing per-image fitting time by $3.0\times$--$15.7\times$ compared with representative Gaussian codecs. It further maintains high-throughput deterministic decoding at $175.9$--$478.2$~MP/s. These results demonstrate that content-adaptive Gaussian compression can substantially reduce per-image optimization cost while preserving strong reconstruction quality and efficient explicit decoding.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04411

TxBench: A Benchmark of Attack Transactions and Evidence-Backed Negatives on Seven EVM Chains

Tamer Abdelaziz, karim Ali

原文摘要

Researchers evaluate detectors of malicious smart-contract transactions on datasets that each paper assembles anew. Their positives come from incident trackers, whose citations mix attacks with setup steps and transactions that never ran, or from a detector's own output, which makes the evaluation circular; their negatives are ordinary transactions. A shared benchmark needs positives whose own execution shows the attack and negatives that use the attacks' mechanisms, with no detector deciding either label. We present TxBench, a benchmark of attack transactions and evidence-backed negatives on seven EVM chains, with the five-step pipeline that builds it. The pipeline collects every transaction that BlockSec's incident library or the EVM Hack Registry cites, maps its weakness to OWASP's SCWE taxonomy, and rebuilds its execution from free data sources. A cited transaction qualifies as a valid TxBench attack only if it extracts value from others during execution, is consistent with all verified source claims, and is independently linked to the reported incident by corroborating evidence. Negative examples are sampled from each chain's traffic near the attacks and retained only when positive evidence confirms their legitimacy. Of 1,850 cited transactions, TxBench accepts 1,012 attacks from 602 incidents, worth USD 868.1 million: 846 contract exploits and 166 key compromises. A blind review under rules fixed in advance confirms 98 of 100 sampled attacks (97.7% weighted) and all 100 sampled negatives. As baselines, four zero-shot LLMs reading anonymized traces are precise but find at most 0.43 of the attacks and no key compromise, leaving ample room for better detectors.

cs.CR

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