Introduces APV framework and Bayesian PIIE to evaluate and enhance LLMs' reasoning about pedagogical intent, reporting strong discrimination and r=0.958 human correlation on instructional tasks.
Stride: Strategic trajectory reasoning via discriminative estimation for verifiable reinforcement learning.arXiv preprint arXiv:2606.15866,
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 7verdicts
UNVERDICTED 7representative citing papers
ReShift is a reasoning-level backdoor framework for VLMs that uses poisoned data construction and joint optimization to shift CoT trajectories on trigger while preserving surface coherence.
DAIN reframes multimodal fusion as dynamic agent collaboration with sparse activation, claiming SOTA results including 2.6% accuracy gain on ADNI across five benchmarks.
ProWAFT proposes a workload-aware dynamic fault-tolerance method for FPGA CNN accelerators via selective TMR and partial reconfiguration, reporting lower composite cost than static TMR or reactive approaches on ResNet/MobileNet traces under SEU injection.
PASE is a neuro-symbolic self-healing system that synthesizes LLM recovery plans, verifies them in simulation, and uses DRL to optimize prompts, claiming over 40% faster recovery on cloud fault data.
EVLA combines a Unified Co-State Encoder and Electro-aware Structured Reasoning Chain with physics-guided training to produce energy-optimal driving decisions, reporting +5.6% accuracy gains over fine-tuned VLM baselines on a driving QA benchmark.
FinInvest-GTCN combines graph, temporal, and causal networks with meta-causal adaptation to improve risk-adjusted predictions for VC investments, achieving RA-MSE of 2.51 and 18.7% higher simulated returns on proprietary data.
citing papers explorer
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Beyond Skepticism: Evaluating LLMs Pedagogical Intent Reasoning with the Adaptive Pedagogical Vigilance Framework
Introduces APV framework and Bayesian PIIE to evaluate and enhance LLMs' reasoning about pedagogical intent, reporting strong discrimination and r=0.958 human correlation on instructional tasks.
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DAIN: Dynamic Agent-Based Interaction Network for Efficient and Collaborative Multimodal Reasoning
DAIN reframes multimodal fusion as dynamic agent collaboration with sparse activation, claiming SOTA results including 2.6% accuracy gain on ADNI across five benchmarks.
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ProWAFT: A ROMA-LPD Instance for Workload-Aware and Dynamic Fault Tolerance in FPGA-Based CNN Accelerators
ProWAFT proposes a workload-aware dynamic fault-tolerance method for FPGA CNN accelerators via selective TMR and partial reconfiguration, reporting lower composite cost than static TMR or reactive approaches on ResNet/MobileNet traces under SEU injection.
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EVLA: An Electro-Aware Multimodal Assistant for Physically-Grounded Driving Reasoning and Control
EVLA combines a Unified Co-State Encoder and Electro-aware Structured Reasoning Chain with physics-guided training to produce energy-optimal driving decisions, reporting +5.6% accuracy gains over fine-tuned VLM baselines on a driving QA benchmark.
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FinInvest-GTCN: Explainable Graph-Temporal-Causal Modeling for Risk-Aware Investment Decision Optimization
FinInvest-GTCN combines graph, temporal, and causal networks with meta-causal adaptation to improve risk-adjusted predictions for VC investments, achieving RA-MSE of 2.51 and 18.7% higher simulated returns on proprietary data.