arXiv subjects

Yogeswar Reddy Thota

Publications and source records attributed to Yogeswar Reddy Thota.

4 recordsLinked to original sources

arXiv2026

LUMOS: A Semantic Operating-System Layer for Accessibility-Grounded AI Agents

Current operating systems expose interfaces optimized for human users but not for AI agents. Humans benefit from pixels, icons, windows, visual grouping, mouse movement, and keyboard shortcuts; AI agents instead need compact semantic state, grounded actions, and reliable feedback. As a result, many computer-use agents are forced to interpret screenshots, OCR output, and visual crops, introducing high token costs, visual ambiguity, latency, and coordinate uncertainty. This paper introduces LUMOS (Language Model Unified Machine-Readable Operating-System Semantics), a semantic interaction layer between AI agents and operating systems. LUMOS converts native accessibility metadata and browser UI structures into machine readable semantic blueprints with stable identifiers, roles, names, values, bounds, and action affordances. It also supports live semantic pointer grounding by querying the UI element under or near the cursor through operating-system automation APIs. An LLM then acts through an accessibility grounded observe act loop using constrained visible-UI primitives rather than application-specific scripts. LUMOS does not claim to replace visual agents; instead, it reduces dependence on screenshots when operating systems already provide semantic structure. These results suggest a path toward AI-native operating systems and machine-readable interaction layers.

cs.OS↗

arXiv2026

When Models Ignore Definitions: Measuring Semantic Override Hallucinations in LLM Reasoning

Large language models (LLMs) demonstrate strong performance on standard digital logic and Boolean reasoning tasks, yet their reliability under locally redefined semantics remains poorly understood. In many formal settings, such as circuit specifications, examinations, and hardware documentation, operators and components are explicitly redefined within narrow scope. Correct reasoning in these contexts requires models to temporarily suppress globally learned conventions in favor of prompt-local definitions. In this work, we study a systematic failure mode we term semantic override, in which an LLM reverts to its pretrained default interpretation of operators or gate behavior despite explicit redefinition in the prompt. We also identify a related class of errors, assumption injection, where models commit to unstated hardware semantics when critical details are underspecified, rather than requesting clarification. We introduce a compact micro-benchmark of 30 logic and digital-circuit reasoning tasks designed as verifier-style traps, spanning Boolean algebra, operator overloading, redefined gates, and circuit-level semantics. Evaluating three frontier LLMs, we observe persistent noncompliance with local specifications, confident but incompatible assumptions, and dropped constraints even in elementary settings. Our findings highlight a gap between surface-level correctness and specification-faithful reasoning, motivating evaluation protocols that explicitly test local unlearning and semantic compliance in formal domains.

cs.AR↗

arXiv2026

Human-AI Interaction: Evaluating LLM Reasoning on Digital Logic Circuit included Graph Problems, in terms of creativity in design and analysis

Large Language Models (LLMs) are increasingly used by undergraduate students as on-demand tutors, yet their reliability on circuit- and diagram-based digital logic problems remains unclear. We present a human- AI study evaluating three widely used LLMs (GPT, Gemini, and Claude) on 10 undergraduate-level digital logic questions spanning non-standard counters, JK-based state transitions, timing diagrams, frequency division, and finite-state machines. Twenty-four students performed pairwise model comparisons, providing per-question judgments on (i) preferred model, (ii) perceived correctness, (iii) consistency, (iv) verbosity, and (v) confidence, along with global ratings of overall model quality, satisfaction across multiple dimensions (e.g., accuracy and clarity), and perceived mental effort required to verify answers. To benchmark technical validity, we applied an independent judge-based evaluation against official solutions for all ten questions, using strict correctness criteria. Results reveal a consistent gap between perceived helpfulness and formal correctness: for the most sequentially demanding problems (Q1- Q7), none of the evaluated LLMs matched the official answers, despite producing confident, well-structured explanations that students often rated favorably. Error analysis indicates that models frequently default to canonical textbook templates (e.g., standard ripple counters) and struggle to translate circuit structure into exact state evolution and timing behavior. These findings suggest that, without verification scaffolds, LLMs may be unreliable for core digital logic topics and can inadvertently reinforce misconceptions in undergraduate instruction.

cs.AR↗

arXiv2025

CosineGate: Semantic Dynamic Routing via Cosine Incompatibility in Residual Networks

Modern deep residual networks perform substantial redundant computation by evaluating all residual blocks for every input, even when identity mappings suffice. We introduce CosineGate, an end-to-end differentiable architecture for dynamic routing in residual networks that uses cosine incompatibility between identity and residual feature representations as a self-supervised skip signal. CosineGate measures semantic redundancy through the Cosine Incompatibility Ratio (CIR), defined as 1 - cos(x, F(x)), and uses Gumbel-Softmax relaxation to enable per-sample, per-block gating during training. A progressive FLOPs regularization term controls average compute usage without destabilizing optimization. On CIFAR-10, CosineGate spans the accuracy-efficiency Pareto frontier: an aggressive configuration achieves 89.9 percent accuracy with 24.1 percent FLOPs savings, a balanced configuration achieves 91.3 percent accuracy with 28.5 percent savings at epoch 160, and a conservative configuration reaches a peak of 93.2 percent accuracy with minimal compute reduction. These results match or exceed ResNet-20 (91.3 percent) while reducing computation, without auxiliary supervision, distillation, or task-specific heuristics. Our results demonstrate that simple geometric measures of feature incompatibility provide a principled and effective signal for dynamic residual routing.

cs.NE↗