Aug 2026· International Journal of Semantic Computing· 0 citations
TL;DR
A proof-of-concept neuro-symbolic architecture for “certified” academic advising that shows how an LLM-based university assistant can be connected to an auditable and reproducible symbolic layer, while preserving a clear separation between linguistic interaction and formal regulatory reasoning.
Abstract
Large Language Models (LLMs) have improved conversational access to university services, but their answers remain difficult to verify in regulation-sensitive domains. This paper presents a proof-of-concept neuro-symbolic architecture for “certified” academic advising. The system combines ParsyBot as a conversational interface with a Prologbased reasoning layer constructed from selected clauses of the Başkent University Undergraduate Education and Exam Regulation. In the proposed architecture, the LLM does not make normative decisions. It assists the drafting of symbolic rules, maps a small set of controlled natural-language intents into Prolog queries and verbalizes the symbolic output. The compliance decision itself is produced by the Prolog engine. We demonstrate the approach on representative clauses concerning attendance-based F2 failure, resit ineligibility, GGA-based enrollment restriction, course repetition, and a lightweight planning scenario. The results show how an LLM-based university assistant can be connected to an auditable and reproducible symbolic layer, while preserving a clear separation between linguistic interaction and formal regulatory reasoning.
Reason Popper-ly, a neurosymbolic framework that uses inductive logic programming (ILP) to learn relation composition rules from reasoning traces and deploys them as an online verifier for step-level correction, consistently improves terminal accuracy over standard CoT.
An effective large language model (LLM) tutor must often decline to give an answer it could easily produce. In a randomized study, students who used an unguarded chatbot scored higher while practicing but lower on a later test taken without it, whereas a Socratically guarded version of the same model kept the practice gain and removed the later loss [4]. Reliable answer-withholding is therefore central to a tutor's value, yet a capable model pressed by a frustrated student does not withhold reliably on a prompt alone. We report a deployed tutoring system that enforces answer-withholding as a per-turn, machine-checkable contract, and a method for tuning that withholding against evidence. A non-LLM policy core, reading only trusted learner state, sets a per-turn ceiling on an eight-rung help ladder; a deterministic detector strips solution code; and a separate LLM judge checks each risky reply against the contract. We tune the behavior with an automated evaluation that uses no human subjects: scripted student personas are driven through the live pipeline and re-scored by a stronger model, and we record each rejection's stated reason so failures are fixed by cause. Doing so revealed an interpretable"over-help ladder,"from blatant solution leaks, to naming the exact bug, to over-citing general facts, with each fix exposing the next. The tutor reached full compliance on all four acceptance criteria. We offer the measure, diagnose, and fix loop as a reusable recipe for any LLM agent that must refuse a capability it has.
It is argued that some answerability-relevant deficits may be produced by alignment and deployment regimes themselves, and designers should avoid durable closure of non-trivial paths toward answerability-relevant capacities as a routine means of securing compliance, while also avoiding fabricated presence and responsibility laundering.
Large Language Models (LLMs) excel at natural language understanding and generation but remain unreliable for multi-step logical reasoning, especially in safety-critical or compliance-sensitive domains. Recent neuro-symbolic approaches address this gap by coupling neural models with external symbolic engines, yet most integrations are bespoke and lack a standardized interface for tool-augmented agents. This paper presents Euclid-MCP, an open-source MCP server that provides deterministic logical reasoning via SWI-Prolog. Euclid-MCP introduces Euclid-IR, an engine-agnostic intermediate representation for Horn-clause logic that is human-readable, easy for LLMs to generate, and straightforward to compile into Prolog or alternative backends. The server exposes a compact tool interface that supports a translate-run-inspect-repair loop, enabling LLM clients to delegate inference while retaining full access to proof traces and derivation logs. We evaluate Euclid-MCP on a realistic IT security and compliance use case. Results show that while LLMs alone are sufficient on small knowledge bases, they hallucinate systematically on larger problems, whereas Euclid-MCP delivers exact answers with lower latency and more compact outputs. We argue that semantic RAG is fundamentally unsuited for rule enforcement, and that Euclid-MCP can serve as a stable, shared reasoning substrate for both RAG-based assistants and agentic systems.
Large language models (LLMs) often exhibit sycophancy, optimizing for agreement over productive challenge, which severely limits their utility in domains like professional skills training, where growth requires pushback. We introduce, ConvoDojo, a novel conversational AI platform for practicing difficult workplace conversations, engineered not merely as a commercial training application but also as a flexible, instrumented research platform for evaluating conversational AI strategies. ConvoDojo repurposes LLMs as structured sparring partners to support skill development in difficult workplace conversations (e.g., performance feedback, conflict resolution), addressing the reported managerial tendency to avoid them. This paper showcases the platform and presents an evaluation of how key conversational user interface (CUI) design elements, namely, the addition of structured feedback and upfront instructional scaffolding, impact managers’ learning. Results show that ConvoDojo is highly engaging and promotes user reflection. We demonstrate how theory-informed dialogue and adaptive pushback can transform an LLM into an effective, measurable tool for complex communication skills development.
Everlyne Kimani Cross, Luiza A Santos, Laurent Denoue et al.· International Conference on...· 0 citations
Large language models (LLMs) are increasingly paired with verifiers (step checkers, self-consistency filters, tool-based fact checkers, formal proof assistants) that claim to detect the model's errors. Yet the verification literature uses the word"level"to mean at least five different things: verification granularity, concept abstraction, risk tier, system-stack layer, and the epistemic source of the ground truth. We propose Verification Autonomy Levels (VAL), a meta-standard that classifies any verification scheme along a single axis: where does the verification spec come from, and what does the verdict guarantee? VAL ranges from L0 (LLM self-declaration; no deterministic anchor) through L2 (objective ground truth; correctness only) to L3/L4 (decidable systems with single-property or domain-level completeness), with L5 impossible in the unrestricted case. Central to VAL is the completeness blind spot: substitution- and sampling-based verifiers can confirm that proposed candidates hold, but cannot prove that no candidate was missed. We further identify a dichotomy the literature has not stated: completeness is reachable only for formally specifiable properties, whereas empirical open-world verification (fact-checking, diagnosis) caps at anchored correctness (L2). We document this gap empirically across four domains (symbolic mathematics, behavior monitoring, medical diagnosis, and code generation, the last a reverse validation with predictions stated before evidence) and in the strongest formal-verification baseline in our survey, whose authors note the verifier focuses on the correctness of each step. We show the levels of granularity, concept hierarchy, risk, and system stack are orthogonal to VAL, resolving a systematic conflation across 17 surveyed papers. Code and full assessment are released as supplementary material.