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T. Rosing

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Preprint Aug 2026

COPA: Continual Preference Optimization for Adaptive Prompt Injection Defense

LLMs remain vulnerable to prompt injection attacks, where adversarial instructions embedded in user inputs or external content manipulate model behavior and bypass safeguards. Existing defenses are predominantly static, relying on fixed alignment objectives or attack-specific filtering mechanisms that require redesign...

Roshan Sood, Onat Gungor, T. Rosing · 2 citations

JetSpec: Breaking the Scaling Ceiling of Speculative Decoding with Parallel Tree Drafting

JetSpec is proposed, a head-based SD framework that combines one-forward drafting efficiency with branch-wise causal conditioning that enables JetSpec to convert larger draft budgets into longer accepted prefixes and higher end-to-end speedup.

Lan-Xiang Hu, Zhao-Xiang Feng, Yulun Wu et al. · 15 citations · ⚡1
#machine learning Preprint Sep 2026

T-SMART: Mechanism-Level Attribution for Tool-Augmented Time-Series Question Answering

T-SMART is presented, a neurosymbolic framework that separates these roles: a frozen LLM interprets questions and selects operations, deterministic tools perform numerical computation, and structured perception is invoked only when needed to provide a controlled framework for analyzing component contributions in neuros...

I. Delgado, Himansi Gupta, B. Khatri et al. · 0 citations
#artificial intelligence Preprint Sep 2026

LIMBO: Lifelong Inference-Time Memory and Budget Optimization for LLM Agents

LIMBO is introduced, the first online framework to the authors' knowledge that treats memory as a controllable inference-time resource and jointly optimizes memory strategy and inference budget for each incoming task, and learns this allocation online in a single pass.

Siddharth Sharma, N. Pandey, Onat Gungor et al. · 1 citation
#artificial intelligence Preprint Jan 2026

LifeAgentBench: Benchmarking LLMs for Long-Horizon, Cross-Dimensional Lifestyle Health Reasoning

LifeAgent is proposed, a tool-augmented reasoning baseline that decomposes complex queries, performs multi-step evidence retrieval, and invokes tools for deterministic aggregation that substantially enhances LLMs'capabilities on challenging reasoning tasks, achieving clear improvements over widely used baselines and sh...

Ye Tian, Zihao Wang, Onat Gungor et al. · 0 citations
Preprint Aug 2026

G-MARK: Grounded Multi-Agent Reasoning for Cooperative Driving via Knowledge Graphs

G-MARK is proposed, a grounded multi-agent reasoning framework that converts cooperative object-centric observations into explicit provenance-aware knowledge graphs (KGs) that preserve object hypotheses together with their source attribution, ego-versus-partner visibility, uncertainty, conflicts, spatial relations, and...

B. Gupta, Onat Gungor, T. Rosing · 0 citations

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