Open and completed research requests

Computational irreducibility is often discussed informally as the inability to shortcut a system’s evolution, but it lacks a unified causal-operational characterization. This project proposes a Temporal Causal Models (TCM)-based framework to define and measure irreducibility in dynamical computations. We will formalize notions of causal depth, simulation shortcut limits, and intervention-based predictability, then evaluate them across synthetic and benchmark sequential systems. The expected impact is a clearer theory of when fast prediction is fundamentally impossible and practical tools to diagnose irreducibility in real computational processes.

Modern LLM coding assistants have evolved into tool-using agents that can read repositories, execute commands, modify infrastructure, and trigger external side effects, expanding the attack surface far beyond classic prompt misuse. This project will produce a deep technical survey that systematizes prompt-injection and related agentic risks across runtime execution and offline codebase design, with a focus on coding, CI/CD, MCP, RAG, and plugin ecosystems. The survey will pair threat taxonomy with defense taxonomy, clearly separating prevention, detection, containment, observability, and recovery controls, and distinguishing model-level mitigations from mandatory architectural controls. It will deliver practical secure workflows, configuration patterns, red-team suites, and a maturity model for teams moving from basic assistants to production-grade semi-autonomous engineering agents. The expected impact is a concrete, implementation-oriented reference that helps organizations deploy AI-assisted development safely without relying on prompt-only security assumptions.

This paper studies the theoretical foundations and system architecture of Quarks, with emphasis on multi-agent orchestration for long-horizon research tasks. The core idea is to model research execution as a contract-bounded computation graph where agents coordinate through typed interfaces, validators, and explicit phase transitions. We formalize roles for orchestrator, executor, and AI-scientist agents, including meta-orchestration policies for rerouting, backtracking, and self-correction under uncertainty. The framework combines persistent memory, prompt/template abstractions, and neural-symbolic control primitives to support reliable multi-stage reasoning over extended runtimes. Expected impact is a principled blueprint for robust “research-as-a-service” systems that are safe, robust, extensible, and empirically measurable.

Current neurosymbolic ASP pipelines require manual encoding redesign (for example carry-based formulations) to unlock treewidth-aware compilation gains. This project extends the dual-backend framework by automatically rewriting ASP constraints into semantically equivalent, lower-treewidth forms and dynamically routing each training instance to enumeration or compilation. The core idea is to combine static graph analysis of grounded programs with a semantics-preserving rewrite library and a learned crossover predictor based on treewidth, model-count proxies, and circuit reuse ratio. The expected impact is to remove expert-dependent encoding effort, broaden exact inference to more tasks, and improve training efficiency without sacrificing correctness.

Decision-makers in engineering, policy, economics, and operations often face trade-offs among conflicting objectives, yet method selection remains fragmented across disciplines. This project will produce a structured survey of mathematical methods that support multi-objective decisions, comparing their assumptions, computational behavior, and practical effectiveness. The approach combines a transparent literature-screening protocol with a common evaluation framework spanning solution quality, interpretability, robustness, and scalability. The expected impact is a decision-oriented reference that helps researchers and practitioners choose appropriate methods for specific problem settings and constraints.

LLM-assisted “vibe coding” accelerates software delivery but introduces new attack surfaces beyond traditional secure SDLC risks. This project will build a practical threat model focused on agentic and copilot-style coding workflows, including prompt injection, context poisoning, supply-chain abuse, and secret leakage. We will evaluate mitigation layers spanning prompt hygiene, permissioning, provenance controls, and CI policy gates. The expected impact is a reproducible security playbook and benchmark that organizations can apply to reduce incident likelihood without losing developer productivity.

We propose a reinforcement learning methodology that combines intrinsic novelty-driven exploration with explicit goal conditioning and extrinsic reward optimization. The core motivation is to avoid myopic exploitation while preserving path optimality for objective goals. The approach uses a dual-value design: one stream for task-optimal return and one for curiosity, with adaptive weighting that decays as goal confidence improves. We expect this to improve sample efficiency, robustness, and discovery of better trajectories in sparse- and deceptive-reward settings. The impact is a practical framework for agents that remain purpose-driven while retaining productive curiosity.

Continual learning systems must remain plastic enough to learn new tasks while stable enough to avoid catastrophic forgetting, but this tradeoff is increasingly constrained by memory behavior rather than compute alone. This project adopts a unified data-access perspective in which deterministic retrieval and stochastic sampling are treated within one framework, so memory support for both adaptation and retention can be analyzed jointly. We will design and evaluate memory mechanisms that explicitly manage stochastic demand, entropy pressure, and replay/update efficiency across task sequences. Expected impact is a clearer systems-level blueprint for continual learning stacks that improve retention and adaptation while remaining robust and efficient on realistic hardware.