arXiv subjects

Ishaan Gangwani

Publications and source records attributed to Ishaan Gangwani.

5 recordsLinked to original sources

arXiv2026

Reason-to-Transmit: Deliberative Adaptive Communication for Cooperative Perception

Cooperative perception among autonomous agents overcomes the limitations of single-agent sensing, but bandwidth constraints in vehicle-to-everything (V2X) networks require efficient communication policies. Existing approaches rely on reactive mechanisms, such as confidence maps, learned gating, or sparse masks, to decide what to transmit, without reasoning about why a message benefits the receiver. We introduce Reason-to-Transmit (R2T), a framework that equips each agent with a lightweight transformer-based module that reasons over local scene context, estimated neighbor information gaps, and bandwidth budget to make per-region transmission decisions. Trained end-to-end with a bandwidth-aware objective, R2T is evaluated against nine baselines in a multi-agent bird's-eye-view perception environment. Any communication improves performance by about 58% AP over no communication. At low bandwidth, all selective methods perform similarly, but R2T shows clear gains under high occlusion, where information asymmetry is greatest, approaching oracle performance. All methods degrade gracefully under packet drops up to 50%, showing robustness to communication failures. These results indicate that while fusion design dominates performance, deliberative communication provides additional gains in challenging scenarios. R2T introduces a reasoning-based approach to communication, enabling more efficient and context-aware information sharing in cooperative perception.

cs.MA↗

arXiv2026

AgentComm-Bench: Stress-Testing Cooperative Embodied AI Under Latency, Packet Loss, and Bandwidth Collapse

Cooperative multi-agent methods for embodied AI are almost universally evaluated under idealized communication: zero latency, no packet loss, and unlimited bandwidth. Real-world deployment on robots with wireless links, autonomous vehicles on congested networks, or drone swarms in contested spectrum offers no such guarantees. We introduce AgentComm-Bench, a benchmark suite and evaluation protocol that systematically stress-tests cooperative embodied AI under six communication impairment dimensions: latency, packet loss, bandwidth collapse, asynchronous updates, stale memory, and conflicting sensor evidence. AgentComm-Bench spans three task families: cooperative perception, multi-agent waypoint navigation, and cooperative zone search, and evaluates five communication strategies, including a lightweight method we propose based on redundant message coding with staleness-aware fusion. Our experiments reveal that communication-dependent tasks degrade catastrophically: stale memory and bandwidth collapse cause over 96% performance drops in navigation, while content corruption (stale or conflicting data) reduces perception F1 by over 85%. Vulnerability depends on the interaction between impairment type and task design; perception fusion is robust to packet loss but amplifies corrupted data. Redundant message coding more than doubles navigation performance under 80% packet loss. We release AgentComm-Bench as a practical evaluation protocol and recommend that cooperative embodied AI work report performance under multiple impairment conditions.

cs.AI↗

arXiv2025

Light-Weight Benchmarks Reveal the Hidden Hardware Cost of Zero-Shot Tabular Foundation Models

Zero-shot foundation models (FMs) promise training-free prediction on tabular data, yet their hardware footprint remains poorly characterized. We present a fully reproducible benchmark that reports test accuracy together with wall-clock latency, peak CPU RAM, and peak GPU VRAM on four public datasets: Adult-Income, Higgs-100k, Wine-Quality, and California-Housing. Two open FMs (TabPFN-1.0 and TabICL-base) are compared against tuned XGBoost, LightGBM, and Random Forest baselines on a single NVIDIA T4 GPU. The tree ensembles equal or surpass FM accuracy on three datasets while completing full-test batches in <= 0.40 s and <= 150 MB RAM, using zero VRAM. TabICL achieves a 0.8 percentage-point gain on Higgs but requires roughly 40,000 times more latency (960 s) and 9 GB VRAM. TabPFN matches tree-model accuracy on Wine and Housing but peaks at 4 GB VRAM and cannot process the full 100k-row Higgs table. These results quantify the substantial hardware-versus-accuracy trade-offs in current tabular FMs and provide an open baseline for future efficiency-oriented research.

cs.LG↗

arXiv2025

MicroProbe: Efficient Reliability Assessment for Foundation Models with Minimal Data

Foundation model reliability assessment typically requires thousands of evaluation examples, making it computationally expensive and time-consuming for real-world deployment. We introduce microprobe, a novel approach that achieves comprehensive reliability assessment using only 100 strategically selected probe examples. Our method combines strategic prompt diversity across five key reliability dimensions with advanced uncertainty quantification and adaptive weighting to efficiently detect potential failure modes. Through extensive empirical evaluation on multiple language models (GPT-2 variants, GPT-2 Medium, GPT-2 Large) and cross-domain validation (healthcare, finance, legal), we demonstrate that microprobe achieves 23.5% higher composite reliability scores compared to random sampling baselines, with exceptional statistical significance (p < 0.001, Cohen's d = 1.21). Expert validation by three AI safety researchers confirms the effectiveness of our strategic selection, rating our approach 4.14/5.0 versus 3.14/5.0 for random selection. microprobe completes reliability assessment with 99.9% statistical power while representing a 90% reduction in assessment cost and maintaining 95% of traditional method coverage. Our approach addresses a critical gap in efficient model evaluation for responsible AI deployment.

cs.AI↗

arXiv2025

Zero-Training Temporal Drift Detection for Transformer Sentiment Models: A Comprehensive Analysis on Authentic Social Media Streams

We present a comprehensive zero-training temporal drift analysis of transformer-based sentiment models validated on authentic social media data from major real-world events. Through systematic evaluation across three transformer architectures and rigorous statistical validation on 12,279 authentic social media posts, we demonstrate significant model instability with accuracy drops reaching 23.4% during event-driven periods. Our analysis reveals maximum confidence drops of 13.0% (Bootstrap 95% CI: [9.1%, 16.5%]) with strong correlation to actual performance degradation. We introduce four novel drift metrics that outperform embedding-based baselines while maintaining computational efficiency suitable for production deployment. Statistical validation across multiple events confirms robust detection capabilities with practical significance exceeding industry monitoring thresholds. This zero-training methodology enables immediate deployment for real-time sentiment monitoring systems and provides new insights into transformer model behavior during dynamic content periods.

cs.LG↗