Learned Memory and Constraint-Aware Decoding for AI Systems

Large AI systems increasingly depend on operational machinery around the model: memory management, context allocation, cache policy, constraint handling, decoding controls, and evidence layers. These components shape cost, latency, reliability, safety posture, and developer experience.

Arxis Systems groups three Tunet Cognition modules into one research thesis. Adaptive-memory explores learned memory management for inference workloads. Pama-cache explores learned KV-cache allocation under memory budgets. Verified-decoding explores differentiable constraint handling, verifier-guided structured outputs, and logit repair during generation.

The newest evidence is narrow but concrete: verified-decoding now includes real-model semantic extraction artifacts and full Outlines schema-baseline comparisons on two small model families. The evidence supports a focused claim about evidence-grounded structured outputs, not a broad claim about frontier-model decoding superiority.

This paper is grounded in local product pages for adaptive-memory, pama-cache, and verified-decoding. Those pages describe predictive prefetching, adaptive compression, intelligent caching, learned KV-cache retention and eviction, tiered memory, differentiable constraints, learned world models, logit repair, and conformal acceptance layers.

The verified-decoding portion is also grounded in package tests, research protocol notes, and dated 2026-06-10 benchmark artifacts. Those artifacts cover 24-case adversarial semantic extraction runs for Pythia 160M, TinyLlama 1.1B, and distilgpt2, plus full Outlines schema-baseline runs for Pythia 160M and distilgpt2.

The paper does not assert broad empirical performance. It translates module behavior into a coherent system architecture, names the evidence now available, and identifies the evidence still needed before external claims should be made.

AI applications are often bottlenecked outside the core model weights. Long-context workflows pressure KV-cache budgets. Repeated inference workflows benefit from memory reuse and prefetching. Safety-sensitive generation needs controls that are stronger than prompting but more flexible than rigid grammar masks.

Static heuristics can be useful, but they struggle when workload, task, context distribution, and risk profile change. Arxis asks whether these surrounding controls can become adaptive, inspectable, and task-aware while remaining measurable and bounded.

The Arxis family can be modeled as three control planes around inference. The memory control plane predicts and allocates reusable information. The cache control plane decides which context representations should remain active, compressed, or evicted. The decoding control plane evaluates and repairs generation steps against constraints and learned world-state expectations.

These planes should remain modular. A product may adopt cache allocation without verified decoding, or verified decoding without adaptive-memory. The shared research direction is explicit interfaces and evidence for controls around models.

Adaptive-memory is described as learned memory management for ML inference. Its mechanisms include access-pattern prediction, importance-aware compression, tier promotion, and prefetching before demand.

The research question is whether learned policy improves useful availability of information under resource constraints. Evaluation should measure hit rate, latency effect, compression loss, policy overhead, workload sensitivity, and failure behavior when predictions are wrong.

Pama-cache is described as learned KV-cache allocation for long-context LLM workflows. It replaces static eviction with learned policy across working, compressed, and associative memory tiers.

The key systems challenge is preserving useful context without letting cache management become opaque or unstable. The policy should expose why tokens, segments, or representations were retained, compressed, or removed.

Verified-decoding is described as differentiable constraint satisfaction for LLM decoding. Rather than relying only on grammar masks or post-hoc rejection, it proposes soft learned constraints, world-model rollouts, gradient-based logit repair, and a conformal acceptance layer.

The strongest current use case is narrower and more practical: evidence-grounded structured output. Grammar and schema systems can make JSON valid while still selecting the wrong source-supported value. Verified-decoding tests whether a verifier can keep the output inside the valid schema while selecting the value supported by evidence.

This is safety-sensitive. Constraint-aware decoding may reduce some invalid generation, but it can also introduce false confidence if constraints are incomplete, learned models are wrong, or acceptance criteria are poorly calibrated.

The current verified-decoding package reports 151 passing tests across the package workspace. Its strongest real-model evidence is a 24-case adversarial semantic extraction suite. On Pythia 160M, TinyLlama 1.1B, and distilgpt2, the evidence-derived semantic_verified method reaches 100% exact match while preserving 100% JSON and schema validity.

The package now includes an opt-in Outlines schema baseline. On full 24-case Pythia 160M and distilgpt2 runs, outlines_schema preserves 100% JSON and schema validity but reaches 25.0% and 20.8% exact match respectively. semantic_verified reaches 100% exact match on the same cases, winning 18 of 24 paired cases on Pythia and 19 of 24 paired cases on distilgpt2, with no paired losses.

This is useful evidence, not final proof. The suite is small and synthetic. The evidence resolver is deterministic. The result should be promoted as an early benchmark signal for evidence-grounded constrained outputs, not as a general claim that Arxis beats Outlines, Guidance, LMQL, or frontier-model structured-output systems.

Frontier model APIs usually do not expose token logits or custom decoding hooks. In that setting, verified-decoding cannot honestly be described as token-level decoding. It should be used as a verifier, reranker, retry controller, tool-call guardrail, or evaluation harness.

A practical frontier-model workflow asks the model for one or more candidate JSON or tool-call outputs, verifies each candidate against schema and source evidence, accepts the best supported candidate, or issues a focused retry when no candidate passes. This is compatible with API models because verification happens after generation.

For open-weight or local models that expose logits, verified-decoding can also act during generation through decoding-time constraints and repair. The paper therefore distinguishes two integration modes: verifier-guided structured output for frontier APIs, and decoding-time constraint methods for local models with token-level access.

Arxis modules require empirical evaluation before strong claims. Adaptive-memory needs workload benchmarks and ablation studies. Pama-cache needs long-context task suites, memory-budget curves, and degradation analysis. Verified-decoding needs larger adversarial suites, full TinyLlama Outlines runs, Guidance and LMQL generation-method baselines, latency measurements, failure examples, and human review for task-specific validity.

The evaluation should measure not only output quality but operational cost. A learned controller that improves one metric while adding unacceptable latency, complexity, or debugging opacity may not be a product improvement.

The main failure modes are prediction error, cache misallocation, over-compression, constraint mismatch, world-model hallucination, evidence opacity, and verifier overreach. In each case, the system may look intelligent while silently making poor operational decisions.

For structured outputs, the most important failure mode is valid-but-wrong JSON: an output can satisfy the schema while selecting a value not supported by the source. This is exactly the gap the verified-decoding benchmark now targets.

Fallbacks matter. Static policies, conservative decoding, hard constraints, retry prompts, or human review may be required when learned controls are uncertain.

Arxis modules should expose small, testable interfaces: memory policy APIs, cache budget controls, decoding constraint adapters, evidence traces, candidate verifiers, retry controllers, and configuration profiles.

An agent using Arxis should be able to explain which memory was loaded, which context was retained, which constraints were applied, which evidence supported the structured output, and why a generation step or candidate answer was accepted or repaired.

Learned operational controls can improve adaptability, but they can also make systems harder to reason about. Without traces, tests, and fallback policy, they may move hidden complexity from hand-written heuristics into learned components.

The immediate verified-decoding roadmap is clear: complete the TinyLlama Outlines comparison, implement Guidance and LMQL as true generation baselines, expand the adversarial suite from dozens to hundreds or thousands of cases, report latency and failure modes, and separate local decoding-time claims from frontier API verifier/reranker claims.

Future work should build benchmark harnesses, evidence traces, fallback interfaces, and documentation that lets developers inspect the behavior of each control plane.

Arxis Systems proposes that memory, cache, and decoding controls around AI models should become explicit system layers. The disciplined claim is not that learned controllers automatically outperform static rules. The claim is that these controls deserve named interfaces, evidence traces, evaluation suites, and bounded fallback behavior.

The newest verified-decoding evidence gives Tunet Cognition a sharper thesis: structure alone is not enough. The next useful layer is evidence-grounded structure, where valid JSON or tool calls must also match the source facts they claim to represent.