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Liquid Conversations: Enhanced Time Awareness and Adaptive Context Management in a Hybrid LLM-LNN Framework

BitwareLabs Research
August 30, 2025

Abstract

Large language models (LLMs) shine in isolated tasks but falter in dynamic dialogues, mishandling pacing, interruptions, temporal cues (like "yesterday" or "post-lunch"), and context under constrained token limits. We introduce Liquid Conversations, a deployable system integrating an LLM with a specialized Liquid Neural Network (LNN) cluster for: (1) Simulated Time Awareness (STA)—a dynamic chronometric state that adjusts reasoning tempo in real-time; and (2) Precise Context Injection (PCI)—a budgeted, granular retrieval mechanism that embeds only essential context snippets per turn.

Leveraging LNNs' adaptive time constants, the system dynamically scales its "internal clock" for quick responses in fast exchanges or deeper reflection in slower ones. PCI treats retrieval as an optimization task across diverse memory types (episodic dialogues, semantic knowledge, procedural logs), delivering concise, targeted prompt enhancements over bloated full-prompt dumps.

Internal benchmarks on extended dialogues show sharp drops in Contextual Error Rate (–35% to –52%), reduced median turn latency (–22%), boosted temporal coherence (+0.7 on a 1–5 scale), and elevated human naturalness scores (+0.5 Likert points), all at neutral or reduced token consumption. We cover architecture, variants, constraints, and ethical considerations, showing how compact LNN wrappers can infuse LLMs with intuitive conversational rhythm—the subtle timing and context smarts that make chats feel alive.

1. Introduction

LLMs generate coherent text but stumble in live talks: bloated prompts lead to forgotten details, ignored time shifts ("later today" vs. "tomorrow"), and unnatural flow. Core issues? Time is tokenized crudely, not modeled as a fluid signal; context gets dumped wholesale, bloating windows and muddying focus.

Liquid Conversations flips this by elevating time and context to tunable controls. A lightweight LNN cluster operates in tandem with the LLM, orchestrating per-turn adaptations via continuous-time states and emitting lean context patches under strict budgets.

Key contributions:
  1. Simulated Time Awareness (STA): A latent chronometric vector c(t) ∈ ℝk that tunes LNN time constants, enabling seamless shifts between reactive and deliberative modes.
  2. Precise Context Injection (PCI): A token-constrained optimizer for span-level retrieval and fusion, prioritizing intent-aligned, time-salient snippets from mixed memories.
  3. Scalable Cluster Design: Millisecond-scale execution for STA/PCI, ensuring low overhead while managing session-long continuity.
  4. Empirical Gains: Validated improvements in error rates, latency, coherence, and perceived naturalness, with token efficiency held steady.

2. Background & Related Work

Liquid Neural Networks (LNNs). Rooted in continuous-time recurrent models and neural ODEs, LNNs feature input-adaptive time constants for efficient, stable dynamics in compact networks. Ideal for real-time control, they outperform traditional RNNs in tasks like time-series forecasting and robotics, with fewer parameters and edge-friendly footprints.

Retrieval-Augmented Generation (RAG). Standard RAG fetches full passages; advanced variants refine via reranking or pruning. We advance this with fine-grained span selection and a budget-aware optimizer for tighter integration.

Temporal Dialogue Modeling. Past approaches embed timestamps or recency scores. STA goes further, using a evolving latent clock to modulate core dynamics, not just inputs.

3. System Overview

Per user turn, a dual-loop activates:

  • Control Loop (LNN Cluster): Updates c(t), infers phase/intent, and crafts a context patch within budget B tokens.
  • Generation Loop (LLM): Takes base prompt + patch + light controls; outputs response.

3.1 Components

  • Chronometric State Store: Captures real-time clocks, turn intervals, session markers, and priors (e.g., peak hours vs. off-peak).
  • LNN Controllers: Compact modules with adaptive τ, outputting tempo, intent embeddings, and context priors.
  • Memory Fabric: Tiered indices for episodic (timed chat fragments), semantic (facts/profiles), and procedural (tools/plans) data.
  • Context Budget Allocator (CBA): Knapsack-style selector maximizing utility under B.
  • Prompt Patcher: Assembles spans into a fixed template with headers and guards.

4. Methods

4.1 Simulated Time Awareness (STA)

Time evolves via latent c(t) ∈ ℝk:

ċ = fθ(c, ut, Δt)

where ut fuses clock data, delays, phases, and optional sentiment. LNN state x updates as:

τ(c, ut, x) · ẋ = -x + σ(Wx + Uut + b)

with τ(·) > 0 input-dependent. High τ promotes reflection; low τ boosts speed.

Outputs include:

  • Tempo λ ∈ [0,1] (reflective to reactive) for LLM params (e.g., temp, max tokens) and CBA tuning.
  • Salience weights favoring timely data.
  • Phase labels for patching.

4.2 Precise Context Injection (PCI)

PCI targets spans (e.g., sentences, blocks). Candidates S = {si} from hybrid search. Scoring:

score(si) = α · sim(q, si) + β · time(si, c) + γ · diversity(si, C) + δ · intent(si, z)

Optimize:

maxP⊆S Σs∈P score(s) s.t. Σs∈P tokens(s) ≤ B

Greedy heuristic (score/token + diversity penalty) runs fast on GPU.

Patch template:

  1. Intent summary (1-2 lines).
  2. Key facts (timed spans).
  3. Recent history (1-3 turns).
  4. Procedural elements.
  5. Constraints (e.g., preferences).

4.3 Training & Calibration

  • Self-Supervision: Counterfactual span tests on dialogues (remove/add, measure impact).
  • Preference Tuning: Human pairwise comparisons for naturalness/accuracy.
  • Priors: Circadian initialization, online refinement.

5. Cluster Architecture

LNNs (under 1M params) deploy as microservices on GPU/CPU, alongside streaming indices, KV-cache, and event bus.

Turn flow:

  1. LNN updates c(t), predicts intent, fetches candidates.
  2. CBA selects.
  3. Patch composes.
  4. LLM decodes with tuned config.

Overhead: <10 ms median on standard hardware.

6. Experiments

6.1 Datasets & Protocol

  • ILD-6k: 6,000+ sessions (10-300 mins) across domains.
  • TQA-1k: 1,000 temporal QA variants.

Baselines: Base LLM, RAG-Doc (chunk-level), LNN-Only (STA sans PCI), Ours.

6.2 Metrics

  • CER: Context-related errors.
  • TTL: First-token delay.
  • TCS: Temporal rating (1-5).
  • HNR: Naturalness (1-7).
  • TE: Utility per token.

6.3 Results

Metric Base RAG-Doc LNN-Only Ours
CER (%) 18.1 15.2 12.4 8.7
TTL (ms p50) 480 590 505 460
TCS 3.1 3.5 3.8 4.2
HNR 4.2 4.5 4.7 5.0
TE (x) 1.0 1.2 1.4 2.1

Relative gains: CER –35% to –52%; TTL –22% vs. RAG; TCS +0.7; HNR +0.5; TE 1.75x.

6.4 Ablations

  • No STA: CER –25%, but TCS/HNR dip.
  • No time term: TCS –0.4.
  • Chunks over spans: TE reverts to baseline.

7. Analysis & Case Studies

  • Rapid Exchanges: Short τ enables snappy replies.
  • Extended Sessions: Expanded τ pulls in checkpoints.
  • Time Phrases: Normalized injections (e.g., "post-lunch as 1-3 PM") build trust.

8. Limitations

  • Cultural time mismatches; mitigated via adaptation.
  • Metadata dependency for salience.
  • Rare tone shifts from diverse spans.
  • Privacy: Strict consent and auditing.

9. Ethics & Safety

Memory scoped to consents; patches auditable with origins. STA interprets but doesn't invent times, with transparent notes. Bias checks in priors/training.

10. Conclusion

By harnessing LNNs for time/context control, Liquid Conversations bridges LLMs to fluid dialogues—cutting errors, latency, and waste. Next: Multi-party handling, affective integration, predictive patching.

Appendix A: CBA Sketch

  1. Fetch spans (hybrid).
  2. Score with weights.
  3. Greedy select (ratio + penalties).
  4. Order stably.
  5. Output JSON for patching.

Appendix B: Implementation Notes

  • LNN: Stacked cells, ODE solver.
  • Indices: Embeddings + BM25 + keys.
  • Latency: <10 ms; LLM-agnostic.

References (Expanded)

  1. Hasani, R. et al. "Liquid Time-Constant Networks." (2021).
  2. Chen, T.Q. et al. "Neural Ordinary Differential Equations." (2018).
  3. Karpukhin, V. et al. "Dense Passage Retrieval." (2020).
  4. Reimers, N., Gurevych, I. "Sentence-BERT." (2019).
  5. Liquid Neural Networks: Fluid, Flexible Neurons. Deepgram (2024).
  6. What Are Liquid Neural Networks? Finextra (2025).
  7. Liquid Neural Networks: Edge Efficient AI. Ajith's AI Pulse (2025).
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