[MS] Update PDF table extraction to support aligned Markdown

[lesyk]

Results of processed PDFs:

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FILE: MEDRPT-2024-PAT-3847_medical_report_scan.pdf
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FILE: RECEIPT-2024-TXN-98765_retail_purchase.pdf
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TECHMART ELECTRONICS
4567 Innovation Blvd
San Francisco, CA 94103
(415) 555-0199

===================================

Store #0342 - Downtown SF
11/23/2024 14:32:18 PST
TXN: TXN-98765-2024
Cashier: Emily Rodriguez
Register: POS-07

-----------------------------------

Wireless Noise-Cancelling
Headphones - Premium Black
AUDIO-5521 1 @ $349.99
Member Discount $-50.00
$299.99
USB-C Hub 7-in-1 Adapter
with HDMI & Ethernet
ACC-8834 2 @ $79.99
$159.98
Portable SSD 2TB
Thunderbolt 3 Compatible
STOR-2241 1 @ $289.00
Member Discount $-29.00
$260.00
Ergonomic Wireless Mouse
Rechargeable Battery
ACC-9012 1 @ $59.99
$59.99
Screen Cleaning Kit
Professional Grade
CARE-1156 3 @ $12.99
$38.97
HDMI 2.1 Cable 6ft
8K Resolution Support
CABLE-7789 2 @ $24.99
Member Discount $-5.00
$44.98
-----------------------------------

SUBTOTAL $863.91
Member Discount (15%)-$84.00
Sales Tax (8.5%) $66.23
Rewards Applied -$25.00
===================================
TOTAL $821.14
===================================

PAYMENT METHOD
Visa Card ending in 4782
Auth: 847392
Ref: REF-20241123-98765

-----------------------------------

REWARDS MEMBER
Sarah Mitchell
ID: TM-447821
Points Earned: 821
Total Points: 3,247
Next Reward: $50 gift card
at 5,000 pts (1,753 to go)

-----------------------------------

RETURN POLICY
Returns within 30 days
Receipt required
Electronics must be unopened

*TXN98765202411231432*

Thank you for shopping!
www.techmart.example.com

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FILE: REPAIR-2022-INV-001_multipage.pdf
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ZAVA AUTO REPAIR
Certified Collision Repair
123 Main Street, Redmond, WA 98052
Phone: (425) 000-0000
Preliminary Estimate (ID: EST-1008)
| Customer Information |                     |     | Vehicle Information |                   |
| -------------------- | ------------------- | --- | ------------------- | ----------------- |
| Insured name         | Gabriel Diaz        |     | Year                | 2022              |
| Claim #              | SF-1008             |     | Make                | Jeep              |
| Policy #             | POL-2022-555        |     | Model               | Grand Cherokee    |
| Phone                | (425) 111-1111      |     | Trim                | Limited           |
| Email                | [email protected] |     | VIN                 | 1C4RJFBG2NC123456 |
|                      |                     |     | Color               | White             |
|                      |                     |     | Odometer            | 9,800             |
| Repair Order #       | RO-20221108         |     | Estimator           | Ellis Turner      |
Estimate Totals
|                  |     | Hours | Rate | Cost  |
| ---------------- | --- | ----- | ---- | ----- |
| Parts            |     |       |      | 2,100 |
| Body Labor       |     | 2     | 150  | 300   |
| Paint Labor      |     | 1.5   | 150  | 225   |
| Mechanical Labor |     | -     | -    | -     |
Supplies
|               | Paint Supplies           |     |        | 60     |
| ------------- | ------------------------ | --- | ------ | ------ |
|               | Body Supplies            |     |        | 30     |
| Other Charges |                          |     |        | 15     |
| Subtotal      |                          |     |        | 2,730  |
| Sales Tax     |                          |     | 10.20% | 278.46 |
| GRAND TOTAL   |                          |     |        | 5,738  |
| Note          | Minor rear bumper repair |     |        |        |
This is a preliminary estimate for the visible damage of the vehicle. Additional damage / repairs / parts may be found
after the vehicle has been disassembled and damaged parts have been removed. Suspension damages may be
present, but can not be determined until an alignment on the vehicle has been done. Parts Prices may vary due to
models and vehicle maker price updates. Please be advised if vehicle owner elects to have vehicle sent to service for
any mechanical concerns, ALL service departments charge a vehicle diagnostic charge. If the mechanical concern is
deemed not related to an insurance claim, vehicle owner will be reponsible for charges.

ZAVA AUTO REPAIR
Certified Collision Repair
123 Main Street, Redmond, WA 98052
Phone: (425) 000-0000
Preliminary Estimate (ID: EST-1008)
Customer Information Vehicle Information
| Insured name   | Bruce Wayne                |     | Year      | 2025         |
| -------------- | -------------------------- | --- | --------- | ------------ |
| Claim #        |

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FILE: SPARSE-2024-INV-1234_borderless_table.pdf
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INVENTORY RECONCILIATION REPORT
Report ID: SPARSE-2024-INV-1234
Warehouse: Distribution Center East
Report Date: 2024-11-15
Prepared By: Sarah Martinez
| Product Code | Location | Expected | Actual | Variance | Status   |
| ------------ | -------- | -------- | ------ | -------- | -------- |
| SKU-8847     | A-12     | 450      |        |          |          |
|              | B-07     |          | 289    | -23      |          |
| SKU-9201     |          | 780      | 778    |          | OK       |
|              | C-15     |          |        | +15      |          |
| SKU-4563     | D-22     |          | 156    |          | CRITICAL |
|              |          | 180      |        | -24      |          |
| SKU-7728     | A-08     | 920      |        |          |          |
|              |          |          | 935    | +15      | OK       |
Variance Analysis:
Summary Statistics:
Total Variance Cost: $4,287.50
Critical Items: 1
Overall Accuracy: 97.2%
Detailed Analysis by Category:
The inventory reconciliation reveals several key findings. The primary variance driver is SKU-4563,
which shows a -24 unit discrepancy requiring immediate investigation. Location B-07 handling of
SKU-8847 also demonstrates significant variance. Cross-location verification protocols should be

reviewed to prevent future discrepancies. The overall accuracy rate of 97.2% meets our target
threshold, but critical items require expedited resolution to maintain operational efficiency.
Extended Inventory Review:
| Product Code | Category    | Unit Cost | Total Value | Last Audit | Notes      |
| ------------ | ----------- | --------- | ----------- | ---------- | ---------- |
| SKU-8847     | Electronics | $45.00    | $13,005.00  | 2024-10-15 |            |
| SKU-9201     | Hardware    | $32.50    | $25,285.00  | 2024-10-22 | Verified   |
| SKU-4563     | Software    | $120.00   | $18,720.00  |            | Critical   |
| SKU-7728     | Accessories | $15.75    | $14,726.25  | 2024-11-01 |            |
| SKU-3345     | Electronics | $67.00    | $22,445.00  | 2024-10-18 |            |
| SKU-5512     | Hardware    | $89.00    | $31,150.00  |            | Pending    |
| SKU-6678     | Software    | $200.00   | $42,000.00  | 2024-10-25 | High Value |
| SKU-7789     | Accessories | $8.50     | $5,950.00   | 2024-11-05 |            |
| SKU-2234     | Electronics | $125.00   | $35,000.00  |            |            |
| SKU-1123     | Hardware    | $55.00    | $27,500.00  | 2024-10-30 | Verified   |
Recommendations:
1. Immediate review of SKU-4563 handling procedures. 2. Implement additional verification for critical
items. 3. Schedule follow-up audit for high-value products (SKU-6678, SKU-2234).
Approval:

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FILE: test.pdf
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1

Introduction

Large language models (LLMs) are becoming a crucial building block in developing powerful agents
that utilize LLMs for reasoning, tool usage, and adapting to new observations (Yao et al., 2022; Xi
et al., 2023; Wang et al., 2023b) in many real-world tasks. Given the expanding tasks that could
benefit from LLMs and the growing task complexity, an intuitive approach to scale up the power of
agents is to use multiple agents that cooperate. Prior work suggests that multiple agents can help
encourage divergent thinking (Liang et al., 2023), improve factuality and reasoning (Du et al., 2023),
and provide validation (Wu et al., 2023). In light of the intuition and early evidence of promise, it is
intriguing to ask the following question: how can we facilitate the development of LLM applications
that could span a broad spectrum of domains and complexities based on the multi-agent approach?

Our insight is to use multi-agent conversations to achieve it. There are at least three reasons con-
firming its general feasibility and utility thanks to recent advances in LLMs: First, because chat-
optimized LLMs (e.g., GPT-4) show the ability to incorporate feedback, LLM agents can cooperate
through conversations with each other or human(s), e.g., a dialog where agents provide and seek rea-
soning, observations, critiques, and validation. Second, because a single LLM can exhibit a broad
range of capabilities (especially when configured with the correct prompt and inference settings),
conversations between differently configured agents can help combine these broad LLM capabilities
in a modular and complementary manner. Third, LLMs have demonstrated ability to solve complex
tasks when the tasks are broken into simpler subtasks. Multi-agent conversations can enable this
partitioning and integration in an intuitive manner. How can we leverage the above insights and
support different applications with the common requirement of coordinating multiple agents, poten-
tially backed by LLMs, humans, or tools exhibiting different capacities? We desire a multi-agent
conversation framework with generic abstraction and effective implementation that has the flexibil-
ity to satisfy different application needs. Achieving this requires addressing two critical questions:
(1) How can we design individual agents that are capable, reusable, customizable, and effective in
multi-agent collaboration? (2) How can we develop a straightforward, unified interface that can
accommodate a wide range of agent conversation patterns? In practice, applications of varying
complexities may need distinct sets of agents with specific capabilities, and may require different
conversation patterns, such as single- or multi-turn dialogs, different human involvement modes, and
static vs. dynamic conversation. Moreover, developers may prefer the flexibility to program agent
interactions in natural language or code. Failing to adequately address these two questions would
limit the framework’s