arXiv subjects

Mohammadali Khodabandehlou

Publications and source records attributed to Mohammadali Khodabandehlou.

4 recordsLinked to original sources

arXiv2026

AKTS: Sub-Microsecond Kernel Policy Switching for Language-Model Agents

GPU-backed LLM servers multiplex interactive requests with background batch work on the same CPUs; a fixed kernel policy serves one objective and loses the other, so agentic OS control needs to switch scheduler behavior as the workload changes. The hard part is applying the switch safely and fast enough for the kernel: scheduler events occur every 1-10 $μ$s, and any code running there must satisfy the eBPF verifier. Scalar knobs are fast but limited, while generating eBPF policy code puts compilation, verification and possible rejection on the runtime path. We present AKTS, which verifies a policy library once, at load time, and reduces the agent's runtime action to writing an integer index into an in-kernel array of preverified policies, resolved by a tail call. Because the agent emits an index rather than code, verifier failure is not a runtime outcome. On Linux 6.14, AKTS applies a policy switch in 920 ns (p50); makes an invalid index inert across 60,217 invocations on an attached scheduler; and switches policies in a vLLM workload to capture 97% of a throughput policy's batch work while matching a latency policy's burst response.

cs.OS↗

arXiv2026

Schedule Repair for DAG Workflows under Link Disruptions

Schedules for directed acyclic graph (DAG) workflows in networked IoT systems are typically computed assuming a static or generally stable network. In contested and adversarial environments, this assumption is not valid. Links degrade and fail due to mobility, interference, and jamming. We study schedule repair: when a link disruption invalidates part of a schedule, how much of it should be rescheduled? We introduce a spectrum of repair policies that vary in repair scope, how much of the pending schedule each may move: wait out the disruption, reroute data around it, reschedule only the affected tasks locally, or reschedule all pending tasks globally. We evaluate each against an oracle and charge every repair a decision latency proportional to the extent to which it moves. Across 100 workload instances spanning synthetic task graphs, RIoTBench pipelines, and WfCommons scientific workflows, each run at five communication-to-computation ratios (CCRs) and disrupted by processes with deliberately different correlation structure, we find that no single scope wins: rerouting nearly erases isolated failures that cost waiting 30%, global repair comes within 4% of the oracle under jamming blackouts, waiting is favored under memoryless link flapping for larger and communication-heavy workloads (the scheduling analog of route-flap damping), self-healing mobility outages reward patience over reaction, and accounting for repair latency erodes large scopes first. We conclude that the scope of the repair should be adapted to the disruption process and the repair cost, rather than fixed by the scheduler.

cs.DC↗

arXiv2026

Compression-Aware Abstention: Teaching LLMs to Refuse When KV-Compression Masks Remove Answer Evidence

KV-cache compression reduces LLM inference memory by evicting context tokens, but when the evicted tokens contain answer-bearing evidence, the model may hallucinate instead of recognizing that the compressed context is insufficient. We address this failure from a behavioral perspective: to our knowledge, this is the first work to formulate compression-aware abstention as a learning problem, in which a model learns to answer when supporting evidence survives compression and abstain when it does not. We construct supervision from compressor survival masks and tight answer-bearing spans, labeling examples as Confident when evidence survives and Abstain when it is removed. A 10.1M-parameter LoRA adapter trained on ~2.6K MuSiQue 2-hop QA examples reduces base-model hallucinations by 97% under prompt-style truncation while preserving correct answering on evidence-retaining examples. Unlike prompt-only abstention baselines, which over-abstain on many answerable high-retention examples, the trained adapter learns a conditional policy. We also evaluate the method under actual compressed-cache decoding, where multi-compressor training yields a 6-22x relative lift over the unaided base on evidence-retaining examples. Controlled-deletion experiments show that the learned behavior is driven by evidence content rather than input length alone.

cs.CL↗

arXiv2026

Studying the Effect of Schedule Preemption on Dynamic Task Graph Scheduling

Dynamic scheduling of task graphs is often addressed without revisiting prior task allocations, with a primary focus on minimizing makespan. We study controlled schedule preemption, introducing the Last-K Preemption model, which selectively reschedules recent task graphs while preserving earlier allocations. Using synthetic, RIoTBench, WFCommons, and adversarial workloads, we compare preemptive, non-preemptive, and partial-preemptive strategies across makespan, fairness, utilization, and runtime. Results show moderate preemption can match most makespan and utilization gains of full preemption while maintaining fairness and low overhead.

cs.DC↗