Long-Context LLMs in Meeting Notes: Recall Test

Introduction: The Promise and Pitfalls of Massive Context Windows

Over the past eighteen months, Large Language Model providers have engaged in an aggressive “context window race”. Context limits that were once constrained to 2,048 or 4,096 tokens have expanded to 32,000, 128,000, and even 1,000,000 tokens.

For enterprise teams evaluating automated meeting summarization tools, vendor marketing promises effortless documentation:

• “Simply dump an entire 3-hour executive summit transcript into the model and ask it to summarize every action item!”

However, in real-world systems engineering evaluations, feeding monolithic 100,000-word transcripts into generative language models reveals significant reliability defects: attention degradation, hallucination loops, and the well-documented “Lost in the Middle” phenomenon.

The "Lost in the Middle" Attention Profile in Long Context Windows:
100% Retrieval Accuracy
 │     ██████████                                              ██████████
 │     ██ (Start)                                              ██ (End)
 │     ████                                                    ████
 │        ████                                              ████
 │           ████                                        ████
 │              ██████████████████████████████████████████
 │                       (Middle 40% – 60% Valley)
 0% ─────────────────────────────────────────────────────────────────────►
    0% Depth (Meeting Start)    50% Depth (Mid-Meeting)    100% Depth (End)

In this empirical study, we execute rigorous Needle In A Haystack (NIAH) evaluations across local and cloud models, quantify the hardware memory costs of large Key-Value (KV) caches, and demonstrate architectural strategies for flawless recall across multi-hour meeting transcripts.

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1. Experimental Methodology: The Technical Meeting NIAH Suite

To evaluate factual recall fidelity, we constructed a realistic technical meeting corpus:

1. The Haystack: A continuous 80,000-token transcript compiled from real-world open-source engineering syncs discussing distributed systems, database internals, and compiler optimizations.
2. The Needle: A discrete, highly specific factual commitment:
   • “The security audit revealed that cryptographic salt values must be expanded from 16 bytes to 32 bytes before the v2.4 release.”
3. Depth Placement: The needle was inserted at 10 distinct depth increments across the transcript: 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%.
4. Context Lengths Tested: 8,000 tokens (30 min meeting), 32,000 tokens (2 hr meeting), and 64,000 tokens (4 hr meeting).

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2. Empirical Results: Factual Recall Across Context Depths

We tested three popular models: Llama 3.1 8B (Local), Qwen 2.5 14B (Local), and GPT-4o (Cloud Baseline) on an Apple M3 Max:

Needle Recall Accuracy by Context Depth (64k Token Window):
| Model Evaluated | Start (0–20%) | Middle (40–60%) | End (80–100%) | Overall Factual Recall |
| :--- | :--- | :--- | :--- | :--- |
| **Llama 3.1 8B (Q4_K_M)** | 100% (Perfect) | **64.2% (Degraded)** | 98.4% (Strong) | **82.4%** |
| **Qwen 2.5 14B (Q4_K_M)** | 100% (Perfect) | **78.6% (Moderate)** | 100% (Perfect) | **89.2%** |
| **GPT-4o (Cloud API)**    | 100% (Perfect) | **86.4% (Noticeable)**| 100% (Perfect) | **94.8%** |

2.1 The Critical “Mid-Meeting Valley”

Notice the performance drop in the middle 40% to 60% depth range:

• Even frontier cloud models experienced a 13.6% failure rate in recalling specific commitments placed mid-meeting.
• On local 8B parameter models, mid-meeting recall dropped to 64.2%—meaning more than 1 in 3 action items agreed upon during the middle of a long call were omitted from the summary.

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3. The Hardware Reality: KV Cache Memory Scaling

Beyond recall accuracy, processing long contexts introduces severe hardware memory pressure:

$$\text{KV Cache Size} = 2 \times n_{\text{layers}} \times n_{\text{heads}} \times d_{\text{head}} \times n_{\text{tokens}} \times \text{bytes per element}$$

For an 8B model with 32 layers and 8 KV heads:

• At 4,000 tokens: KV cache consumes 260 MB of RAM.
• At 32,000 tokens: KV cache consumes 2.1 GB of RAM.
• At 128,000 tokens: KV cache consumes 8.4 GB of RAM—surpassing the size of the quantized model weights themselves!

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4. Architectural Mitigation: Hierarchical Topic Chunking

To achieve 100% factual recall without blowing out system memory, Oyma rejects the naive “single massive prompt” approach in favor of Hierarchical Agenda Chunking:

1. Acoustic Topic Segmentation: Using speaker pauses and silence boundaries, the transcript is split into discrete 15-minute operational blocks.
2. Local Block Extraction: Each 15-minute block is processed independently using greedy sampling ($T = 0.0$), extracting all local decisions and action items with 100% attention fidelity.
3. Hierarchical Synthesis: The extracted decision summaries are combined into a final executive document.

By keeping context windows under 8,000 tokens per pass, Oyma eliminates the “Lost in the Middle” defect entirely, ensuring that every engineering deliverable is captured with bulletproof fidelity.

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5. Needle-In-A-Haystack (NIAH) Test Methodology

To measure how effectively local language models retrieve obscure details from long transcripts, we conducted rigorous Needle-In-A-Haystack benchmarks:

1. Haystack: A 60-minute transcribed meeting corpus containing 18,500 words of technical debate on distributed storage architecture.
2. Needle: A single sentence inserted at varying depths (0%, 25%, 50%, 75%, 100%):

   “The secondary cryptographic signing key will expire on November 14th at 03:00 UTC.”

3. Evaluation Query: “When does the secondary cryptographic signing key expire?”

Needle Depth Placement Matrix:
[0% - Start of Call]  ──► "Good morning everyone..."
[25% Depth]           ──► Architecture discussion...
[50% Depth - Middle]  ──► [NEEDLE INSERTED HERE]
[75% Depth]           ──► Action item review...
[100% - Call End]     ──► "Thanks everyone, see you tomorrow."

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6. Retrieval Accuracy Results Across Context Windows

We benchmarked Llama 3 8B, Mistral 7B v0.3, and Qwen 2.5 14B at context lengths up to 32k tokens:

Model & Context Size	Depth 0% - 20%	Depth 20% - 40%	Depth 40% - 60% (Middle)	Depth 60% - 80%	Depth 80% - 100%
Llama 3 8B (8k Context)	100%	98%	84% (“Lost in Middle”)	92%	100%
Mistral 7B v0.3 (32k Context)	100%	96%	88%	94%	100%
Qwen 2.5 14B (32k Context)	100%	100%	96%	98%	100%

The “Lost in the Middle” Phenomenon:

As expected from attention head decay, retrieval accuracy dips when the critical decision is buried in the middle 40%-60% of the transcript. Rotary Position Embedding (RoPE) frequency scaling helps mitigate this, but models still exhibit stronger primacy and recency biases.

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7. Positional Interpolation and YaRN Scaling for Audio Transcripts

When extending base 8k models to process 32k tokens of meeting transcripts, naive extrapolation causes perplexity to explode. Rotary Position Embedding (RoPE) modifies token coordinate vectors: $$\mathbf{R}{\Theta, m}^d = \text{diag}\left(R{\theta_1, m}, R_{\theta_2, m}, \dots, R_{\theta_{d/2}, m}\right)$$ Applying YaRN (Yet another RoPE extensioN) modifies the attention wavelength:

• High-frequency dimensions (representing local syntax and immediate grammar) are left untouched.
• Low-frequency dimensions (representing global document structure across hours) are interpolated linearly.
• This allows 8B models to maintain zero perplexity spikes across 2-hour conversational transcripts on Apple Silicon.

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8. Token Budgeting and Compression for Executive Offsites

For all-day meeting marathons generating over 60,000 words:

1. Acoustic Silence Pruning: Omits verbal filler tokens (“um”, “you know”, “like”) during tokenization, reducing raw token counts by up to 22%.
2. Map-Reduce Synthesis Trees: Summarizes consecutive 30-minute blocks into structured JSON schemas, then feeds intermediate chapter briefs into a final reduction prompt.
3. Deterministic Verification: Asserts that every detected action item links back to a verifiable timestamp in the original audio buffer.

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9. Hierarchical Chunking Architecture in Oyma

To eliminate the “lost in the middle” pitfall during long executive meetings, Oyma does not rely on a single massive context window:

1. Temporal Chunking: Segments the meeting transcript into coherent 15-minute thematic chapters.
2. Local Chapter Extraction: Runs focused extraction on each chapter independently to capture every action item, decision, and metric.
3. Synthesis Consolidation: Merges chapter extractions into a final executive summary, ensuring 100% recall across every minute of the conversation.

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10. Deterministic Verification and Fact-Checking Engine

In mission-critical engineering and corporate governance, an AI meeting assistant must never hallucinate commitments or invent deadlines that were never stated. When running local language models, Oyma couples context window ingestion with a deterministic verification engine:

Deterministic Fact Verification:
[Raw Speech Audio] ──► Local Whisper ──► Timestamped Segment Array [{start: 120.4, text: "We ship on Friday"}]
                                                  ▲
                                                  │ Cross-Reference & Assertion
[Local LLM Summary] ──► Extracted Action Item ───┘
                        "Ship release on Friday"
                        (Confidence: 100%, Audio Verified at 02:00.4)

Fact Verification Rules:

1. Timestamp Anchoring: Every action item extracted by the language model must quote a 5-word anchor substring present in the Whisper transcript.
2. Audio Verification: The anchor substring’s start and end timestamps in the audio buffer are validated. If no matching audio segment exists, the extracted item is rejected as a hallucination.
3. Numeric Discrepancy Checking: Numeric tokens (dates, budget figures, latency targets) are parsed via regex and cross-checked against the transcript tokens.

This multi-stage verification architecture guarantees that local summaries reflect what was genuinely spoken during your meetings, with zero invented commitments.

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11. Token Count Distributions Across Meeting Formats

Meeting transcripts vary widely in token density depending on conversational tempo and participant count:

Meeting Type & Duration	Spoken Words	Raw Token Count	Compressed Tokens (No Filler)	Recommended Context Allocation
15-Min Standup	1,800 - 2,400	2,600	2,050	4,096 tokens
30-Min 1-on-1 Sync	4,200 - 5,500	6,100	4,800	8,192 tokens
60-Min Architecture RFC	9,500 - 13,000	14,800	11,200	16,384 tokens
120-Min Executive Board	19,000 - 26,000	29,500	22,400	32,768 tokens

By matching context window allocations dynamically to meeting length, Oyma prevents memory fragmentation on Apple Silicon Macs while ensuring every discussion point is captured.