Human-Computer Interaction
From Segments to Trajectories: Evolving Affective Graphs with Evidence Retrieval for Continuous EEG Emotion Recognition
Chi Yang, Jihong Wang, Chengxi Xie et al. (+5 more)
Electroencephalography (
EEG)-based
emotion recognition is important for affective computing and human-computer interaction, yet most existing methods divide a long trial into short segments and assign each segment the label of its source trial. Although this strategy increases the number of training samples, it reduces an evolving emotional response to a segment-level, coarse-grained, and static prediction problem. In reality,
emotion may continuously emerge, intensify, weaken, and fluctuate as a stimulus unfolds, motivating the prediction of a time-aligned affective trajectory from the complete
EEG trial. This task requires coordinated modeling of how spatial neural organization evolves throughout the trial and how local emotional fluctuations interact with longer-term trends. In this work, we formally define and systematically investigate continuous
EEG emotion recognition as whole-trial affective trajectory prediction. We propose EAGER, an Evolving Affective Graph framework with Evidence
Retrieval for continuous
EEG emotion recognition. EAGER comprises two complementary modules: Affective State-guided
Topology Evolution models the evolving spatial organization of
EEG activity, while Multi-scale Temporal Evidence
Retrieval integrates short-term fluctuations with longer-range temporal trends for time-aligned prediction. Experiments on MAHNOB-HCI, SEED-VII, and REFED show consistent gains in trajectory-tracking metrics over representative methods, with competitive pointwise errors.
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All authors Chi Yang, Jihong Wang, Chengxi Xie, Kai He, Huan Liu, Man Yao, Shile Qi, Yuzhe Zhang
Robotics
PHASE: Compliance-Enabled Tactile Phase Retrieval for Few-Shot Insertion Learning
Jeremy Siburian, Cristian C. Beltran-Hernandez, Tatsuya Matsushima et al. (+3 more)
Contact-rich assembly tasks such as peg-in-hole insertion remain difficult to learn from limited demonstrations. While
retrieval-augmented imitation learning, which augments target demonstrations with relevant prior data, offers a promising direction, its applicability to contact-rich manipulation remains largely unexplored. Contact-rich insertion unfolds over multiple phases from search to insert, and retrieving phase-specific experience from prior data in principled ways remains an open question. Our key insight is that a compliant wrist enables the
robot to sustain contact throughout execution, producing rich tactile and force signals that naturally reveal the phase structure of insertion and inform what should be retrieved. Based on this insight, we present PHASE (PHase-Aware
Segmentation and
REtrieval), a framework for compliance-enabled tactile phase
retrieval that integrates
multimodal contact-aware representation learning, variable-length phase
segmentation from tactile signals, and phase-consistent
retrieval for policy learning. We evaluate PHASE on real-world peg-in-hole insertion across five peg geometries, comparing against
retrieval strategies drawn from state-of-the-art methods under a shared policy architecture. PHASE improves the overall success rate by 13 percentage points over the strongest non-phase-aware baseline, and improves performance under unseen initial positions by 30 percentage points. These results demonstrate that aligning
retrieval with interaction-defined contact phases substantially improves robustness in few-shot insertion learning.
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All authors Jeremy Siburian, Cristian C. Beltran-Hernandez, Tatsuya Matsushima, Yusuke Iwasawa, Masashi Hamaya, Mai Nishimura
Combinatorics
Breaking the Infinite Barrier in the $\frac{1}{3}$–$\frac{2}{3}$ Conjecture
Max Aires, Swee Hong Chan, Igor Pak et al. (+1 more)
The balance constant is a poset parameter measuring how well random linear extensions can be split according to their relative order on two elements. We give an $\varepsilon$-improvement for the balance constant over the bound by Brightwell, Felsner and Trotter (1995), for some small $\varepsilon>0$. This is the first general result towards the $\frac{1}{3}$–$\frac{2}{3}$
conjecture in over 30 years.
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All authors Max Aires, Swee Hong Chan, Igor Pak, Greta Panova
Artificial Intelligence
From Tapping to Hopping: Augmenting Mobile GUI Agents with App-Native Deeplinks
Yuchen Sun, Chenglin Cai, Gongjie Zhang et al. (+8 more)
Mobile GUI
agents complete tasks using GUI actions like taps and swipes. These actions are broadly applicable across applications, but reaching a
navigation interface. A single deeplink call can replace a sequence of screen-by-screen GUI actions. We therefore introduce hybrid interaction, using deeplinks for direct
navigation and GUI actions for other on-screen operations and fallback. To enable this, we discover candidate deeplinks through static analysis, validate them on real devices, and describe their observed landing screens. This process creates a verified and grounded deeplink catalog that pairs each working deeplink with a description of its landing screen. Using this catalog, we introduce GUI-Hopper, a improves task success in commercial applications on real devices, further demonstrating the benefits of hybrid interaction.
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All authors Yuchen Sun, Chenglin Cai, Gongjie Zhang, Tianyu Xia, Quyu Kong, Panrong Tong, Zhengwen Zeng, Long Chen, Steven Hoi, Chongyang Zhang, Yue Wang
Machine Learning (Statistics)
Conformal Prediction under Exponential-Tilt Joint Shift
Seungjin Choi
Conformal prediction can lose coverage when the data distribution changes after deployment. We study adaptation using labeled source data and unlabeled target inputs, allowing both the input distribution and its relationship with outcomes to change. We use Exponential Tilt Reweighting
Alignment (ExTRA), introduced for classification by Maity et al. (2023), to estimate structured distribution shifts. We compare using its estimated weights in conformal calibration with additionally tilting the source predictive distribution. Shared learned predictors, estimated weights, calibration samples, and test observations isolate the effect of tilting. Existing theory gives both procedures target coverage with true weights and a common coverage bound with estimated weights. Identification calculations and an analysis of how scoring interacts with weight estimation error help explain why their performance can nevertheless differ. In a synthetic regression setting where the assumed models match the data-generating process and target inputs are informative about the shift, tilting reduces mean set length by about $30\%$ relative to weighting alone, with both methods attaining coverage near nominal. Tilting can instead cause substantial coverage losses in synthetic classification and in regression when target inputs provide little information about the response shift. Real-data experiments also show no consistent benefit. Good coverage from weighted calibration alone does not ensure that adding predictive tilting will preserve coverage. Deciding when to apply this additional adjustment using only source labels and target inputs remains an open problem.
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Optimization and Control
Retraction-Based Gradient Projection Algorithms on Manifolds
Conglong Xu and Hao Wu
We introduce a framework for retraction-based convex optimization on Riemannian
manifolds, which includes a notion of retraction-specific convex sets and retraction-based gradient projection
algorithms. The standard theory of gradient projection
algorithms generalizes easily to this framework. Within this framework, we establish convergence results for retraction-based gradient projection
algorithms with various stepsize rules. As an application, we use our framework to study the weighted low-rank approximation. We also provide numerical validation of our convergence results on the image completion task.
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Machine Learning
CacheReforge: Bounded Recovery for Stale KV Caches under Evolving Adapters
Yuhang Cao, Yanzhou Mu, Chunrong Fang et al. (+1 more)
Large
language models rely on KV caching to reduce repeated prefill computation in long context and interactive applications. As lightweight adapters evolve, cached states reflect earlier versions, so stale reuse distorts current model outputs, while complete affected suffix recomputation restores fidelity at substantial cost. We seek minimal recomputation that recovers current adapter behavior. Existing systems track
token, context, or stable adapter identity, but neither represent caches from earlier adapter versions nor distinguish update propagation from the recomputation required for behavioral recovery. To address these gaps, we introduce CacheReforge, which represents stale KV caches as layerwise mixed-version objects. It combines per-layer adapter anchors, calibrated sensitivity, accumulated drift, and executable restart boundaries to select direct reuse, bounded recomputation, or complete affected-suffix recovery. We distinguish dependency depth from the functional recomputation horizon and use cumulative tail influence to characterize when bounded recovery preserves current-model behavior. We evaluate CacheReforge on Qwen2.5-1.5B and Qwen2.5-7B with continual LoRA updates, including 16K HotpotQA and 2WikiMQA workloads. CacheReforge reduces mean KL divergence by 92.4% relative to stale reuse, while recomputing only 5.44% of layers and reducing cache-maintenance time by 93.2% relative to fresh full prefill. These results show that version-aware recovery preserves model fidelity and most KV caching gains.
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All authors Yuhang Cao, Yanzhou Mu, Chunrong Fang, Zhenyu Chen
Physics and Society
Warned alike, AI agents avoid the less-crowded road while people take it
Takahiro Ezaki, Naoto Imura and Katsuhiro Nishinari
AI
agents built on a few shared models increasingly act for many people. A shared forecast about others can align their choices and change how scarce capacity is allocated. We tested this feedback in a two-road congestion game. Adding one sentence warning that others might follow a routing tip made populations of 50 GPT
agents crowd one road while avoiding the nearly empty alternative. Average travel time rose from 64 to 95 min, although any crowded-road
agent could have saved 69 min by switching alone. The warning discouraged the very move it predicted. The pattern persisted for 100 rounds. Two other model families shifted the same way without locking onto one road. Twelve all-human groups (240 participants) stayed near balance under numerical reports or the tip and warning. In 24 mixed groups with a further 240 participants, imbalance grew with the share of
agents in the registered analysis, while people increasingly took the road the
agents avoided. Collective costs stayed below the allagent reference, but with 15
agents and 5 humans,
agent seats averaged 80 min, compared with 44 min for human seats. Shared forecasts can thus sustain collective inefficiency among similar
agents. A better group average can also hide an unequal burden. Evaluations of AI
agents that share resources should test populations, treat messages as interventions and report who bears the costs.
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Computation and Language
Effects of Transcript Compression on LLM-based Medical Misinformation Detection in Japanese YouTube Videos
Yuya Wake, Sho Tsugawa and Toshiyuki Amagasa
Large
language models (LLMs) are increasingly used to assess long-form medical
videos, but their effectiveness may depend on whether transcripts are provided in full or compressed through summarization,
retrieval, or claim screening. This study examines how such transcript compression affects LLM-based veracity classification of Japanese medical YouTube
videos. We compare four transcript input designs: full transcripts, LLM-generated summaries, RAPTOR-based retrievalaugmented generation (RAG), and Screening, which extracts candidate medical and health-related sentences. Using 74 long-form
videos labeled as Real or Fake, we evaluate classification performance and analyze linguistic changes using J-LIWC, hedge expressions, and institutional or technical terms. The full-transcript Baseline achieved the best performance, whereas all compressed inputs increased false negatives, meaning that Fake
videos were more likely to be misclassified as Real. Summary caused the largest performance drop, while Screening performed best among the compressed inputs but still omitted many medically relevant sentences. Linguistic analyses showed that these errors were not explained by a simple increase in certainty. Instead, Summary reduced affective, social, temporal, cognitive, and conversational cues, while Summary and RAG made institutional and technical terms more salient. These findings suggest that transcript compression can represent Fake
videos as more coherent and authoritative inputs, thereby weakening cues needed for misinformation detection
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Solar and Stellar Astrophysics
Beating and coupling in pulsating stars: A unified Fourier description and observational diagnostics
József M. Benkő and Emese Plachy
Long-term amplitude and phase variations in pulsating
stars produce side peaks or multiplets in the
Fourier spectrum of the light curves around the pulsation frequency and its harmonics. Such structures are often attributed to modulation, although linear beating and the non-linear coupling of close oscillations can also produce similar patterns. We develop a unified analytical
Fourier description of these alternatives and identify observational relations that retain discriminatory power when their frequency patterns overlap. We represent the signal as a dominant, non-sinusoidal oscillation plus one or more secondary oscillations close to it, and analytically derive the frequencies, complex amplitudes, and phases resulting from linear superposition, quadratic and general non-linear coupling. We also illustrate the calculated phenomena using artificial light curves. We implement the resulting tests in a diagnostic tool "freqdiag", which compares modulation with primary–secondary quadratic coupling models anchored to either the higher- or lower-frequency side peak, using the same normalized complex amplitudes, and supplements the fit with hierarchical tests of side-peak amplitude progression. A sinusoidal secondary signal produces only one additional peak in the linear-beating case, whereas a non-sinusoidal secondary signal produces a harmonic sequence, whose separation from the primary harmonics increases with order. Quadratic coupling produces terms $nf_0+lf_B$, but their amplitudes and phases follow shifted-harmonic and combination relations. At general non-linear order, coupling and modulation occupy the same frequency grid. The formalism provides a practical framework for interpreting Blazhko-like RRc
stars, HADS variables, and other pulsating
stars in which modulation and coupling are difficult to separate observationally.
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Artificial Intelligence
EXAONE Demand 1.0: A Time Series Foundation Model for Demand Forecasting
Seunghan Lee, Sangjun Han, Jun Seo et al. (+9 more)
Time series foundation models (TSFMs) are pretrained on series from diverse domains, where demand series make up only a small fraction. Demand data has properties that such corpora rarely contain: Short histories, frequent zeros, censoring by stock-outs, and exogenous events that the series does not record. To this end, we propose EXAONE Demand, built on 1) a demand-specific corpus and 2) a demand-aware adapter. For the corpus, we assemble 11.3M series and 48.4B observations from 73 sources, and a synthetic generator supplies the behaviour that open demand data under-represents. For the adapter, we attach low-rank branches to a frozen general-domain backbone, one for each of the four demand classes (smooth, intermittent, erratic, and lumpy), and a router that reads eight scale-free statistics of the input series decides how much each branch contributes. We build EXAONE Demand in two versions, one trained on real-world and synthetic demand together and one trained on the synthetic corpus alone. On 22 held-out
datasets, both versions outperform 36 TSFMs, and real-world demand adds a gain over synthetic data alone.
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All authors Seunghan Lee, Sangjun Han, Jun Seo, Junhyeok Kang, Jaehoon Lee, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Minjae Kim, Sungdong Yoo, Soonyoung Lee, Wonbin Ahn
Statistical Methodology
Joint-Sparse Transfer Learning for High-Dimensional Multi-Output Regression
Sunwoo Lim and Mladen Kolar
Multitask linear models can improve estimation and prediction by exploiting structure shared across responses, bridging taskwise fitting and complete pooling. In many applications, however, the objective is estimation in a data-limited target domain, while data-rich but heterogeneous source domains are available. Borrowing from these sources can improve efficiency but introduce bias. We develop a joint-sparse transfer-learning framework for high-dimensional multi-output regression that combines shared predictor structure across responses with source-target similarity. The framework yields two complementary
estimators: a fused
estimator that aggregates jointly fitted domain-specific coefficients and a target-based debiased
estimator that adjusts for source-induced shifts. Our error bounds show how transfer increases the available information and sharing predictors across responses reduces selection costs. They also reveal a tradeoff: the fused
estimator benefits from larger sources but may retain bias if source shifts point in similar directions, whereas debiasing trades this bias for additional estimation error governed by the smaller target sample. Comparison with a minimax lower bound identifies regimes where the bounds match up to logarithmic factors, where matching remains unresolved, and where projection onto a target-based convex set closes the gap. Simulations and an analysis of single-
cell RNA and surface-
protein profiles across
cell types support the theory.
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Artificial Intelligence
TISD: On-Policy Self-Distillation with Trajectory Intervention
Taeckyung Lee, Rinat Amankos, Jeonghye Kim et al. (+3 more)
On-policy self-
distillation (OPSD) provides dense teacher targets, but evaluates them only along student-sampled rollouts. When the privileged teacher favors an alternative action at a visited prefix, OPSD can provide a target for the branch decision but cannot supervise the successor contexts induced by that action unless the student samples it. This creates a training-time data-collection bottleneck and suggests a different role for teacher-student disagreement: proposing a trajectory branch rather than identifying a sufficient local repair. Our diagnostic framework using controlled
token interventions reveals that a teacher-preferred
token at peak disagreement can improve student continuation success, while its local corrective value is limited. Motivated by this finding, we introduce a simple branch-regenerate-distill
algorithm, Trajectory-Intervention Self-
Distillation (TISD). TISD forces a teacher-selected branch action, returns suffix generation to the student, and distills the full trajectory under the privileged-context-conditioned teacher. Across the coding models, TISD improves average Avg@4 over SDPO by 1.2 percentage points. Across the science domains, it improves average Avg@128 by 0.8 points under an equal-step budget and by 0.3 points under an equal-time budget. These results support teacher-guided branching as a way to expose useful successor contexts for self-
distillation.
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All authors Taeckyung Lee, Rinat Amankos, Jeonghye Kim, Hyungjun Yoon, Woogyeol Jin, Sung-Ju Lee
Optimization and Control
Tight Stochastic Condition-Number Dependence in Nonconvex-Strongly-Concave Minimax Optimization
Qihao Zhou
We study whether the linear condition-number dependence in the
stochastic complexity of SAPD+ is necessary for nonconvex-strongly-concave minimax optimization. For jointly $L$-smooth objectives with dual strong-concavity parameter $\mu$, we prove a lower bound that matches the SAPD+ upper bound under the same Moreau-envelope stationarity criterion and the same primal-dual initialization gap. Specifically, when $\sigma\ge\varepsilon$, the worst-case complexity of zero-respecting
algorithms is $\Theta(\kappa LG\sigma^2\varepsilon^{-4})$ in the stated accuracy regime, where $\kappa=L/\mu$, $G$ bounds the initial primal-dual gap, and $\sigma^2$ bounds the variance of a general unbiased first-order oracle. The lower bound is realized on a smooth problem class with a bounded dual box. Our construction routes each link of a nonconvex zero-chain through a dual gradient of magnitude proportional to $\varepsilon/\sqrt{\kappa}$, while an undiscovered primal coordinate prevents stationarity. It also yields the primal-gradient lower bound $\Omega(L\Delta(\sqrt{\kappa}\varepsilon^{-2}+\kappa\sigma^2\varepsilon^{-4}))$ after combination with the known deterministic bound, where $\Delta$ bounds the initial primal function gap.
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Numerical Analysis
A vanishing-viscosity minimizing movement Fourier spectral method for scalar conservation laws
Lun Ji, Buyang Li and Fangyan Yao
We propose a vanishing-viscosity minimizing movement (VVMM)
Fourier spectral method for scalar
hyperbolic conservation laws under periodic boundary conditions. The method is a fully discrete space–time residual-minimizing scheme,
Fourier spectral in space and
polynomial in time, stabilized by vanishing viscosity and heat-kernel postprocessing. A basic analytical difficulty in
Fourier discretizations of nonlinear conservation laws is the interaction between
Fourier projection and the nonlinear flux; the resulting projection residual contains unresolved high-frequency components. VVMM addresses this issue by selecting, on each time slab, the numerical solution that minimizes {a penalized cut-off viscous conservation laws residual} and then applies heat-kernel postprocessing to damp unresolved modes. We prove a fully discrete $L^1$ error estimate, in any fixed space dimension, that separates the vanishing-viscosity error, the temporal interpolation error, and the spatial
Fourier truncation error. {Under exact minimization of the penalized cut-off residual and suitable parameter coupling,} the estimate yields, for each fixed $\gamma>0$, an $L^1$ convergence rate $N^{-1/2+\gamma}$, with a constant depending on $\gamma$. Numerical experiments in one and two dimensions illustrate stable shock capturing without visible Gibbs oscillations and convergence behavior consistent with the theory.
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Quantum Physics
Daming Li
We simulate three-
flavor neutrino oscillations on two-
qubit quantum circuits. The
flavor space is embedded as $\ket{00}=\nu_e$, $\ket{01}=\nu_\mu$, $\ket{10}=\nu_\tau$ with the $\ket{11}$ channel kept inert, and the input mixing parameters (NuFIT~5.3, normal ordering, with SK-atm) follow the global fit. We verify three physical settings against analytic or exact references:
vacuum propagation, reproduced by a mass-eigenstate phase
circuit to $\sim 10^{-16}$; constant-density Mikheev–Smirnov–Wolfenstein (MSW) matter effects, showing resonant
flavor conversion when the matter potential $A$ crosses $\Delta m^2_{31}$; and varying-density (
supernova shock-shell) propagation by slicing plus second-order Suzuki–Trotter splitting, with per-slice Trotter error $\sim 10^{-6}$ and total accuracy $\sim 10^{-4}$ against the continuous profile. We report a Suzuki–Trotter precision–resource calibration varying order, step number, and
matrix/probability error. It delimits brute-force Trotterization on
solar baselines: we quantify both standard alternatives, coherence averaging and adiabatic MSW. We also compute
quantum-information diagnostics from the same model parameters, namely mode
entanglement and the CP asymmetry. Finally, we extend the framework to open
quantum systems via a Lindblad master equation with dephasing and absorptive channels. These results provide a reproducible link between low-energy oscillation observables and
quantum-information measures.
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Quantum Physics
From Received Power to Certified Secret Keys: A General Method for Bridging Classical FSO Link Budgets and Decoy-State QKD
Hasan Abbas Al-Mohammed
Received optical power determines
photon arrival flux, but
photon arrival flux does not certify a
quantum key. This paper gives a deployment-independent interface from a classical free-space optical (FSO) link budget to phase-randomized weak coherent pulse BB84, decoy-state estimation, and composable finite-key postprocessing. The channel interface separates
geometric collection, atmospheric extinction, residual pointing, and detector efficiency, and states when common
geometric and pointing expressions are approximations. The finite-key output follows the decoy-state analysis of Lim et al., with key- and test-basis counts estimated separately and an infinite-decoy asymptotic reference. The numerical study covers a five-level attenuation sweep, basis- and
vacuum-probability sensitivities, a conditional Chernoff comparison, and integer-count Monte Carlo. For $3.33 \times 10^8$ emitted pulses, the Hoeffding-based predicted one-kilobit-per-second distance limits are 2.727, 1.883, 1.472, 1.236, and 0.962 km from very clear to dense-fog parameterizations. These are conditional performance predictions, not experimental security certificates. Satellite, drone, high-altitude-platform, terrestrial, and train links enter through the same interface but require their own propagation, acquisition, and device inputs. The framework integrates established security tools into an engineering procedure without claiming a new security proof or a universal range.
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Robotics
A Second Torque Port for Series Elastic Actuators: Parallel-Integrated Design and Time-Scale Torque Allocation
Donghao Jia, Xiubo Xia, Junhang Liu et al. (+3 more)
A series elastic actuator has a single torque port and pays for it twice: the geared motor must swing its own reflected inertia through the spring, so the amplitude it delivers collapses as $\omega^{-2}$ in the command frequency $\omega$ once it saturates, while commands below the transmission's breakaway friction never arrive at all. This letter opens a second torque port on the load side, placing a frameless direct-drive micro motor in parallel with a fixed-stiffness spring – a parallel-integrated SEA, or Pi-SEA, whose delivered torque is read from spring deflection and micro current without a sensor – and dividing the commanded torque between the two channels by time scale rather than by filter design. The micro torque loop is the fast subsystem, which makes the closed loop singularly perturbed and turns the separation the channels need into a bound to check rather than a crossover to tune; a leaky mid-ranging integrator returns the steady load to the spring; and the amplitude ceiling, read backwards, becomes a closed-form sizing rule that matches spring, geared motor and micro motor to the amplitudes and frequencies an application asks for. Against SEAs, the Pi-SEA widens the tracked band at small amplitudes and lowers the residual the joint imposes on its environment, each by an order of magnitude.
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All authors Donghao Jia, Xiubo Xia, Junhang Liu, Zeyu Zhu, Xiaoyu Geng, Jian Sun
Algebraic Topology
The homology of $\mathrm{Out}(F_8)$ and $\mathrm{Aut}(F_7)$
Maroš Grego, Tomáš Vítek and Thomas Willwacher
We compute the rational homology of $\mathrm{Out}(F_8)$ and $\mathrm{Aut}(F_7)$, and several related groups. We also show that the Eisenstein classes in $H_7(\mathrm{Aut}(F_5))$ and $H_{11}(\mathrm{Aut}(F_7))$ are zero.
Strongly Correlated Electrons
Systematic Evolution of Magnetic Anisotropy and Crystal-electric-field Ground States in SmTr2Ge2
Ryuji Higashinaka, Kokoro Higo, Tatsuma D. Matsuda et al. (+1 more)
We investigated the
crystal-electric-field (CEF) states in SmTr2Ge2 (Tr= Co-Cu and Ag) with the ThCr2Si2-type structure by magnetization and specific heat measurements. The
magnetic anisotropy evolves systematically with Tr, changing from an easy ab plane for Tr = Co and Ni to an easy c axis for Tr = Cu and Ag. Combined analyses of the low-temperature susceptibility and
magnetic entropy indicate a level crossing of the CEF ground state between Ni and Cu, together with a systematic
evolution of the first excited CEF energy. The
evolution of the
magnetic properties correlates with changes in local structural parameters, providing an experimental basis for discussing how the ligand environment around the Sm ion is related to the 4f electronic state.
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All authors Ryuji Higashinaka, Kokoro Higo, Tatsuma D. Matsuda, Yuji Aoki
Number Theory
Counterexamples to the inhomogeneous Duffin-Schaeffer conjecture for a residual set of shifts
Yubin He and Lingmin Liao
Let $\theta\in\mathbb Q\setminus\{0\}$. We construct an approximating function $\psi:\mathbb N\to[0,\frac12)$ for which \[ \sum_{q=1}^{\infty}\frac{\varphi(q)}{q}\psi(q)=\infty, \] but the set of $x\in[0,1]$ for which \[ |qx-a-\theta|<\psi(q),\qquad \gcd(a,q)=1, \] holds for infinitely many $(a,q)\in\mathbb Z\times\mathbb N$ has Lebesgue measure zero. Thus the inhomogeneous analogue of the Duffin–Schaeffer
conjecture fails for every nonzero rational shift. By a modification of the construction, we further show that the set of shifts for which the inhomogeneous Duffin–Schaeffer
conjecture fails is residual in $\mathbb R$.
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Chemical Physics
Observing Phase Space Evolution During Unimolecular Decay
Abhirami Vijayakumar, Raidel Martin Barrios, Meenu Upadhyay et al. (+4 more)
Direct time-resolved observation of large amplitude dynamics during ground state statistical unimolecular decay remains an experimental challenge. Here, an experimental Time-Resolved Coulomb Explosion Imaging (TR-CEI) study (J. Chem. Phys. 151, 174301, 2019) of the canonical unimolecular reaction NO$_2$ $\rightarrow$ NO($^2\Pi, v=0, J)$ + O($^3P_j$) at threshold dissociation energies is modeled and analyzed. Quasi-classical trajectory (QCT) simulations on an accurate and validated ground state potential energy surface (PES), with energy and angular momentum constraints from known product state distributions, are used to characterize the dissociation dynamics. TR-CEI projects suddenly, via ultrafast Strong Field Ionization (SFI), the evolving neutral ground state wavepacket onto the repulsive doubly charged cationic state which then impulsively fragments into the NO$^{+}$ + O$^{+}$ ion pair. Within the Coulomb
recoil approximation, which applies at longer NO–O (Jacobi) separations, the ion pair CEI-energy is inversely proportional to the scalar distance between the neutral ground state NO and O moieties at the moment of SFI, thus providing a direct measure of the evolving average NO–O distance during unimolecular decay. Results from QCT simulations agree favourably with the TR-CEI measurements. Phase space
evolution, recurrence time distributions and Lyapunov exponent analysis are presented which support the conclusion that TR-CEI directly observes phase space
evolution during unimolecular decay. For the present system, the time scale for the phase space distribution to achieve statisticality is estimated to be 2 to 3 ps.
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All authors Abhirami Vijayakumar, Raidel Martin Barrios, Meenu Upadhyay, JingChun Wang, Andre Staudte, Albert Stolow, Markus Meuwly
Robotics
VLaRL: Augmenting Vision-Language-Action Models with Simulation-Trained Latent-Conditioned Residual RL
Namiko Saito, Kinam Kim, Heecheol Kim et al. (+2 more)
Vision-
language-action (VLA) models provide broad, instruction-conditioned manipulation behaviors, but their physical execution can remain imprecise during contact-rich interaction. Residual reinforcement learning (RL) can correct such errors while keeping the VLA frozen, but real-
robot RL is costly and safety-critical. We propose VLA Latent-Conditioned RL (VLaRL), which enables residual RL for frozen VLAs to be trained in simulation and deployed on real
robots without real-world RL or online adaptation. The key challenge is transferring the learned residual policy despite the visual gap between simulation and reality. Rather than requiring pixel-level visual correspondence, VLaRL uses the VLA's internal
vision-
language latent representation to condition residual control and as the sim-to-real transfer interface, and learns a lightweight mapper that transforms simulation-derived latents toward the real latent distribution. Across four contact-rich manipulation tasks and two VLA backbones, VLaRL improves real-world success in all task-backbone combinations, while controlled ablations demonstrate the importance of both latent conditioning and latent
alignment for transferring simulation-trained residual control.
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All authors Namiko Saito, Kinam Kim, Heecheol Kim, Katsushi Ikeuchi, Yasuyuki Matsushita
Computation and Language
Evidence-Grounded Auditing of Identification Assumptions in Climate-Policy Causal Evaluations
Yonghong Zhang, Yong Xie, Isabel M. Parra et al. (+1 more)
Difference-in-differences (DID) studies are widely used to evaluate
climate policy, but assessing the evidence supporting their identification assumptions remains challenging. We introduce ARGUS, a structured
language-model pipeline that audits reported evidence against an eleven-dimension assumption-implication-evidence rubric and abstains when relevant evidence cannot be retrieved. We evaluate ARGUS using injected flaws, economics papers, and a small pilot with reconciled labels. On the 11-flaw
benchmark, ARGUS detects 73% of planted flaws, compared with 18% for a keyword-based pipeline. Across 26 economics papers, ARGUS abstains on about 40% of paper-dimension assessments for lack of retrievable evidence. In a five-paper pilot with labels reconciled by two annotators, it assigns a higher risk level than the labels on 25 of the 33 assessments it completes. A rule fixed before the labels arrived removes most of this in-sample; weighted agreement stays low. ARGUS provides evidence-linked risk reports that localize potential weaknesses for expert review, without adjudicating causal claims. Code and data: https://github.com/yonghongzhang-io/ARGUS
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All authors Yonghong Zhang, Yong Xie, Isabel M. Parra, Ricardo Correia
Materials Science
Universal Drift Correction for Multidimensional Scanning Microscopy
Sangjoon Lee, William Millsaps, Dasol Yoon et al. (+7 more)
In scanning microscopy, drift causes the specimen to be sampled at positions displaced from the nominal probe positions. This displacement alters the spatial assignment of the recorded signals and biases quantitative measurements across two-dimensional imaging, channel-resolved spectroscopic mapping, and scan-position-resolved diffraction analysis. Here, we extend orthogonal-scan drift correction from 2D images to spectrum images and diffraction
datasets. We demonstrate how to recover probe positions using either differently oriented multidimensional scans or structural reference images. The recovered positions are used either to resample the multidimensional data onto a regular grid or to assign each recorded signal to its corrected coordinate. Our method combines affine and non-rigid correction, requires no prior structural model, and is implemented as open-source, GPU-accelerated software that reduces processing times by two to three orders of magnitude, enabling routine and automated drift correction for quantitative multidimensional microscopy.
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All authors Sangjoon Lee, William Millsaps, Dasol Yoon, Caitlyn Obrero, Guoliang Hu, Corrie Barnes, Cedric Lim, Andrew Barnum, Arthur R. C. McCray, Colin Ophus
Computer Vision
Reliability-Regulated Trajectory Optimization for Progressive COLMAP-Free 3D Gaussian Splatting
Zijian Wu, Jinliang Wang, Zidian Lin et al. (+5 more)
COLMAP-free 3D Gaussian Splatting (3DGS) bypasses computationally expensive structure-from-motion (SfM) pipelines, yet progressive camera pose tracking remains fundamentally vulnerable to error compounding—early pairwise tracking inaccuracies both corrupt subsequent frame initializations and remain permanently frozen in the scene representation. Rather than relying on heavyweight external neural priors or treating progressive tracking through isolated heuristic fixes, we propose a unified reliability-regulated trajectory optimization framework for progressive COLMAP-free 3DGS. At its core, our framework establishes an intrinsic, self-supervised bidirectional cycle-consistency mechanism that systematically regulates progressive camera trajectory estimation across two complementary temporal horizons: (1) Forward Motion Propagation, where the online reliability signal adaptively gates first-order kinematic warm-starts of rigid motion into upcoming pairwise registrations, supplying informed directional search priors while safely intercepting untrusted transitions; and (2) Retrospective Trajectory Correction, where the same reliability signal dynamically weights relative-pose consistency constraints within a sliding window of neighboring camera poses. By governing both prospective state initialization and retrospective trajectory consolidation through a unified reliability regulator, our self-contained framework resolves progressive drift without external priors or offline preprocessing. Extensive evaluations on Tanks and Temples and CO3D-V2
benchmarks show that our method substantially improves camera trajectory accuracy and novel-view rendering quality, outperforming existing unposed baselines. Code is available at https://github.com/Zijian1026/RRTO-CF3DGS.
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All authors Zijian Wu, Jinliang Wang, Zidian Lin, Ying Song, Ziqian Lu, Hanjie Ma, Zhen Ye, Mingfeng Jiang
Computation and Language
Haixu Ma, Saad Lahrichi, Weiwei Li et al. (+11 more)
Black-box On-Policy
Distillation (OPD) seeks to improve a student from its own generations when the teacher provides sampled responses but not
token probabilities.
Adversarial distillation offers one route: it learns a discriminator over
prompt-matched teacher and student responses and uses its score as the policy reward. However, sampling discriminator negatives from the latest student at each step couples the learned reward to a negative distribution that changes after every policy update. We address this moving-target problem with persistent-negative
adversarial distillation, a live-pool method that replaces a fraction of each discriminator batch with historical,
prompt-matched teacher–student comparisons. Under matched discriminator compute, historical comparisons train the discriminator, while GRPO remains on-policy with fresh student responses. Our analysis identifies the Bayes-optimal reward as a teacher-to-negative log-density ratio and, under explicit assumptions, shows how persistent negatives anchor the discriminator and reduce reward-estimation MSE relative to fresh-negative training. Across two student families, three judges, and four judged-chat
benchmarks, persistent-negative
adversarial distillation consistently improves performance over current methods at matched discriminator compute. It also yields smoother fresh-policy discriminator trajectories, with fewer below-chance dips. These findings identify the discriminator's negative distribution as an important design axis in black-box on-policy
distillation.
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All authors Haixu Ma, Saad Lahrichi, Weiwei Li, Kevin Han, Weiqiang Wu, Peggy Yang, Dongzhuo Li, Ruiyi Li, Serena Li, Gedi Zhou, Mingze Gao, Abhishek Kumar, Xiangjun Fan, Lizhu Zhang
Software Engineering
Developing a Roadmap to an AI-first Organization: A Case Study in Embedded Software Development
Viktor Kjellberg, Srijita Basu, Simin Sun et al. (+2 more)
The emergence of AI
agents is expected to reshape software engineering by moving beyond AI as assistants towards systems capable of planning, executing, and evaluating development tasks with increasing autonomy. This transition is particularly significant for embedded software organizations, where strict requirements for quality, traceability, verification, and long-term maintainability often apply. This paper presents a case study of a large embedded systems company and its transition toward becoming an AI-first organization. Through a mixed method, we analyzed data collected from a semi-structured workshop with 40 participants, including scrum masters, architects, management, and product owners. The findings show that the participants expect agentic AI to affect team structure, required competencies, organizational strategies, and developers' roles within the organization. Based on these findings, the paper discusses implications for
federated AI team formation, human-in-the-loop practices in such an organization, and the sustainable adoption of AI
agents in embedded software engineering. We also present a concrete roadmap for the organization towards becoming an AI-first organization.
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All authors Viktor Kjellberg, Srijita Basu, Simin Sun, Farnaz Fotrousi, Miroslaw Staron
Performance
Evaluation of portability and performance of an OpenMP5 offloaded Quantum-Inspired Evolutionary Optimization Across the GPU Ecosystem
Kasturi Venkata Srikanth, Ashish Singh, Ferdin Sagai Don Bosco et al. (+4 more)
Quantum-inspired evolutionary optimization (QIEO) is a new class of population-based metaheuristic optimization
algorithms which represents design variables as a set of
qubits and searches a continuous, multi-dimensional landscape through rotation of the
qubit's amplitude pair. Every generation rotates those amplitudes toward a single elite, which corresponds to that generation's best. The per-generation cost scales as $O(N_p N_g)$ for $N_p$ chromosomes and $N_g$ genes (decision variables). Production use of such solvers is rarely confined to a single machine class. Prototypes are run on laboratory servers- before moving to rented cloud workstations for more involved campaigns. The largest problems are reserved for leadership-class accelerators. This paper asks whether a single OpenMP~5 source of QIEO, offloaded with \#pragma omp target, is a viable production path in each of those settings. We report three independent, campaigns of the 0/1 knapsack problem against a same-source multi-core Intel CPU baseline. The study comprises approximately 3,000 runs spanning varying chromosome and gene counts, evaluated using both chromosome-level and gene-level offload strategies on the NVIDIA Tesla V100 SXM2, NVIDIA A100 80GB, and AMD Instinct MI300X GPUs. Deployment-specific nuances such as Volta's constant-
memory cliffs, Ampere's L2 persistence and cp.async, CDNA~3's Infinity Cache and XCD occupancy are addressed to ensure high performance of these platforms. Results reveal gene-parallel offload achieved
geometric-mean speedups of 90$\times$, 136$\times$, and 155$\times$ over a single CPU core on the V100, A100, and MI300X, respectively, and 12$\times$, 17$\times$, and 16.6$\times$ over 72 host threads. Furthermore DetermineElite, the $O(N_p)$ selection of the generation-best chromosome, is found to be better suited to the host than to the device.
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All authors Kasturi Venkata Srikanth, Ashish Singh, Ferdin Sagai Don Bosco, Aman Mittal, Abhishek Singh, Aditya Singh, Abhishek Chopra
Artificial Intelligence
SkillEvoReg: Regularizing Agent Skill Evolution Against Overfitting
Guanyu Nie, Fangzhou Zhu, Shixiong Kai et al. (+3 more)
Language-model
agents increasingly improve by converting execution experience into reusable external skills. Yet repeated skill updates form a learning process of their own: locally useful edits can accumulate into redundant or task-specific instructions, while new updates can disrupt behavior that previously worked. We study this problem as skill-
evolution overfitting and introduce SkillEvoReg, a general regularization framework for skill
evolution inspired by anti-overfitting techniques in neural-network training. SkillEvoReg combines training-time skill dropout, which perturbs update generation, and complexity-aware local regularization, which controls unnecessary structural growth, with causal counterexample validation (CCV), which provides targeted behavioral validation of candidate-specific regressions. We instantiate the framework across heterogeneous skill-
evolution systems while retaining each system's native skill evolver and task evaluator. Across SkillOpt, SkillEvolBench, and ContinualSkillBench, SkillEvoReg consistently controls skill-state growth while preserving competitive downstream capability, improves several transfer and later-stage
evolution outcomes, and identifies update-level regressions that structural metrics alone cannot reveal. These results suggest that explicit regularization is a useful complement to increasingly capable skill updaters.
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All authors Guanyu Nie, Fangzhou Zhu, Shixiong Kai, Xiongwei Han, Tao Zhong, Mingxuan Yuan