arXiv:2610.10061v1 Announce Type: cross Abstract: This paper presents TRACK (Telemetry-Based Racing Analysis and Coaching Kit), which is a framework for analyzing driving performance in sim racing an...
arXiv:2607. 06495v1 Announce Type: cross Abstract: Live sports commentary is grounded generation under a deadline: statements concern real, named athletes, the grounding state changes every few seconds, and no reference text exists at generation time.
arXiv:2606. 12616v1 Announce Type: new Abstract: Closed-loop driving simulators typically populate their environments with non-ego traffic agents that behave largely the same way, produced either by rule-based traffic managers or by learned models trained toward a single behavioral mode.
arXiv:2607. 26061v1 Announce Type: new Abstract: Pre-match tactical decision-making in professional football relies heavily on subjective expert analysis and identity-based scouting systems that cannot generalize to unseen teams.
The paper introduces Context-driven Personalized ACC (CoP-ACC), a data‑driven framework that learns from drivers’ throttle overrides to tailor Adaptive Cruise Control behavior. It uses unsupervised clustering to identify representative acceleration profiles, a context classifier to select the appropriate profile based on pre‑maneuver conditions, and a residual regressor to smooth the final profile. Evaluations on real‑world public‑road data show that CoP-ACC reconstructs driver‑expected acceleration patterns more accurately than a standard forced‑ACC baseline, suggesting it can reduce manual interventions and improve ride comfort.
The paper proposes a method for verifying whether two gameplay replays in Counter‑Strike 2 belong to the same player by extracting a behavioral fingerprint that captures crosshair control, movement, economy, combat, and rhythm. Using a six‑fold evaluation on two datasets (Perfect and Professional), the pairwise model achieves ROC AUCs of 0.926 and 0.956, with aiming and low‑level mechanics providing the strongest identity signals. Aggregating evidence across multiple historical demos further improves account‑history AUC, reaching 0.982 on Perfect and 0.975 on Professional.
SpeedrunBench is a new benchmark that tests large language model agents on their ability to develop and refine strategies for completing nine different video games as quickly as possible. The benchmark requires agents to repeatedly improve, reflect, and reason over long action horizons to beat both themselves and other players. Experiments show that while agents can approach human records on simple platformers, they lag behind humans on longer, more complex games within realistic resource limits.
The paper introduces a landmark‑based approach to classify injury‑associated athlete sessions using minute‑resolution multimodal football monitoring data. Instead of labeling every minute, it creates a single representation per athlete‑session at fixed time points (landmarks) based on information up to that moment, thereby aligning the session‑level injury label with the data. Experiments on 3,743 elite women’s football sessions show that cumulative and dynamic logistic regression models achieve ROC‑AUCs between 0.367 and 0.607, though results carry wide uncertainty.
The paper introduces an edge‑assisted crowdsensing framework that uses handlebar‑mounted cameras to capture street‑level video, a vision‑language model to extract structured traffic attributes, and an explainable boosting machine to combine these with OpenStreetMap data and on‑bike kinematics for a 1‑9 safety score. On 1,362 annotated frames from two London routes, the model achieves 72.2 % binary accuracy and a mean absolute error of 1.011, with feature attribution revealing the impact of lane separation, painted lanes, and construction sites on safety ratings. The system operates efficiently at the network edge, requiring only 2.49 s per frame for extraction and 0.4 ms for scoring, and supports continuous monitoring at a cost of 1.4 accuracy points for 1,000 riders over a 20‑second interval.
arXiv:2604. 01608v4 Announce Type: replace Abstract: Multi-agent systems (MAS) tackle complex tasks by distributing expertise, though this often comes at the cost of heavy coordination overhead, context fragmentation, and brittle phase ordering.
The paper introduces a landmark-based approach for classifying injury-associated athlete sessions using minute-resolution multimodal football monitoring data. Instead of labeling every minute, it creates a single representation per athlete-session at fixed time points (landmarks) that incorporates information up to that moment, thereby aligning the session-level injury label with the data. Experiments on 3,743 elite women's football athlete-sessions from the 2020 SoccerMon dataset evaluate various representation strategies and machine learning models, reporting ROC-AUC values between 0.367 and 0.607 and PR-AUC values between 0.0080 and 0.0150, with notable uncertainty across landmarks.
The paper introduces EpiCoach, an agentic system that treats a single athlete as the subject of accountable N‑of‑1 science. It uses a mechanistic digital twin—comprising a lagged heart‑rate model, a fitness‑fatigue model, and a latent skill state—to generate probabilistic predictions for future training sessions, which are logged in an evidence‑gated hypothesis registry. Over eighteen months, the system’s predictions were evaluated both retrospectively and prospectively, showing improved heart‑rate forecasting and a transparent, auditable loop for personalized training guidance.