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Position: Quantum Program Generation Must Prioritize Validity Over Probabilistic Scaling
arXiv:2607.15313v1 Announce Type: new Abstract: The scaling hypothesis assumes that increasing model parameters yields emergent reasoning capabilities. This position paper argues that applying this probabilistic paradigm to generic quantum circuit synthesis is a directional error. Unlike natural languages, quantum circuits require strict adherence to mathematical constraints that manifest a significant syntax-semantics gap. Training on unverified quantum programs means that models learn syntax but fail to capture the physical semantics of the Hilbert space. Since the valid subset of circuit designs decays exponentially with the number of qubits, post-hoc filtering is mathematically intractable. We propose a pivot from human-centric copilots to verifier-centric agents. We integrate hierarchical constraints, topological masks, and symbolic proxies directly into generation. Our analysis suggests that scale alone cannot bridge the validity gap. Verification-aware architectures offer a viab
NeuroCommitSSM: Decision-Centric Shared Autonomy for Safe Assistive Manipulation via EEG-EMG-ET Commit Readiness
arXiv:2607.15395v1 Announce Type: new Abstract: We present NeuroCommitSSM, a decision-centric framework that models when to execute, not just what to do, for safe commit-to-execute control in assistive robotic manipulation. NeuroCommitSSM predicts a continuous commit-readiness score c_t in [0,1] from synchronized electroencephalography (EEG), electromyography (EMG), and eye-tracking (ET), and converts it into discrete commit events through dwell and hysteresis filtering. A three-state finite-state supervisor, HOLD-ASSIST-COMMIT (HAC), gates execution by requiring both a sustained commit-readiness signal from the neural model and real-time perception and robot-state feasibility, including target visibility, inverse kinematics solvability, and collision-free planning, before initiating motion. We evaluate the framework on N=32 subjects performing five activities of daily living (ADL) tasks aligned with the International Classification of Functioning, Disability and Health (ICF), using le
GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis
arXiv:2607.15280v1 Announce Type: new Abstract: Sequential diagnosis requires balancing diagnostic accuracy against resource costs through iterative information gathering. Existing Large Language Model (LLM) approaches exhibit a critical knowledge-reasoning gap: despite encoding extensive medical knowledge, they struggle to reason systematically under cost constraints, often resorting to excessive testing. We propose GraphDx, a knowledge-enhanced framework with two core innovations. First, we design an automated pipeline that leverages LLMs to construct Medical Diagnosis Knowledge Graphs (MDKGs) with quantized typicality, action-centric topology, and dual-objective attributes for both diagnostic relevance and cost-sensitivity. Second, we introduce three collaborative agents (Perception, Reasoning, and Decision) where the Perception and Decision Agents handle language understanding and generation, while the Reasoning Agent performs deterministic evidence scoring and cost-aware planning
Structure of the Circular-Dyadic Convolution Error
arXiv:2607.15293v1 Announce Type: new Abstract: Dyadic and circular convolution can both be computed in $O(N\log N)$ time using the Hadamard transform and the FFT-computed discrete Fourier transform (DFT), respectively. The Hadamard transform is preferable for its real-valued sign flips, yet its substitution for the DFT introduces algebraic error. We present three complementary results that characterize this error. First, we identify exact error cancellation: two input and two output positions are universally error-free, and no reordering of the output can eliminate this error. Second, the error operator is nearly full rank, while its null space has only logarithmic dimension. Third, the expected error is governed by a single alignment scalar, with a closed-form expression obtained by averaging over random filters. In general, the substitution error asymptotically doubles the output energy, except for filters in the universal zero-error subspace, which incur no error. Collectively, the
Why I Refuse to Scale My Coding Agents
You gave a coding agent a task. You went to make coffee, answered an email, or slept. When you came back, it said: Done. All tests pass.Continue reading on Medium »
Lost in Transcription: When the Machine Speaks
Experimenting with Whisper AI Part 5Continue reading on Medium »
Claude Fable produced a counterexample to the Jacobian Conjecture
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AI-Powered Multi-Target Tracking for Drones & Surveillance
Why Modern Surveillance Demands AI-Driven TrackingContinue reading on Medium »
What Machine Learning Is — and What It Is NOT
Machine learning is NOT a way to solve existing problems. ML is a way to solve a new kind of problems.Continue reading on Medium »
10 ChatGPT Prompts Every Entrepreneur Should Use to Save Hours Every Week
How business owners can use AI to write faster, plan better, and automate repetitive tasks.Continue reading on Medium »
Agente de IA no WhatsApp: o que ele já consegue vender sozinho
Responder clientes rapidamente uma das maiores dificuldades de pequenos neg cios. Quando a mensagem fica sem resposta, a inten o de Continue reading on Medium »
ALAYA Lab — 무한한 컨텍스트(Context Growth)의 덫을 깨다: LLM 에이전트를 위한 유한 메모리 규약 ‘AgenticSTS’
1. (Executive Summary)Continue reading on Medium »